Main Questions
Our main question:
- How have global CO2 emission rates changed over time? In particular for the US, and how does the US compare to other countries?
- Are US CO2 emissions, global temperatures, and US storm rates associated?
Open Case Studies : CO2 Emissions Over Time
Michael Ontiveros, Carrie Wright, PhD.
Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.
Motivation
This case study explores how different countries have contributed to Carbon Dioxide (CO2) emissions over time and how CO2 emission rates may relate to increasing global temperatures and increased rates of natural disasters and storms. This report provides a basis for the motivation: https://www.epa.gov/report-environment/greenhouse-gases.
CO2 makes up the largest proportion of greenhouse gas emissions in the United States:
A variety of sources and sectors contribute to greenhouse gas emissions, with transportation contributing the most metric tons of CO2:
So why should we pay attention to greenhouse gases?
According to the US Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks 2020 Report:
Greenhouse gases absorb infrared radiation, thereby trapping heat in the atmosphere and making the planet warmer. The most important greenhouse gases directly emitted by humans include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and several fluorine-containing halogenated substances. Although CO2, CH4, and N2O occur naturally in the atmosphere, human activities have changed their atmospheric concentrations. From the pre- industrial era (i.e., ending about 1750) to 2018, concentrations of these greenhouse gases have increased globally by 46, 165, and 23 percent, respectively (IPCC 2013; NOAA/ESRL 2019a, 2019b, 2019c).
- IPCC stands for the Intergovernmental Panel on Climate Change
There are many signs that our planet is experiencing warmer temperatures:
The connection between greenhouse gas levels and global temperatures and the influence of increased global temperatures on human health are motivated by these reports:
Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program.
- “Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2018.” EPA 430-R-20-002, Tech. Rep. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.
The National Climate Assessment Report states that:
Heat-trapping gases already in the atmosphere have committed us to a hotter future with more climate-related impacts over the next few decades. The magnitude of climate change beyond the next few decades depends primarily on the amount of heat-trapping gases that human activities emit globally, now and in the future.
Learning Objectives
In this case study, we will explore CO2 emission data from around the world. We will also focus on the US specifically to evaluate patterns of temperatures and storm activity. This case study will particularly focus on visualizations of patterns over time. We will especially focus on using packages and functions from the Tidyverse, such as plotlyand gganimate. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.
We will begin by loading the packages that we will need:
library(here)
library(readxl)
library(readr)
library(dplyr)
library(magrittr)
library(tidyverse)
library(plotly)
library(gganimate)
library(RColorBrewer)| Package | Use |
|---|---|
| here | to easily load and save data |
| readxl | to import the excel file data |
| readr | to import the csv file data |
| dplyr | to view and wrangle the data |
| magrittr | to use and reassign data objects using the %<>%pipe operator |
| tidyverse | to wrangle the data and create ggplot2 plots |
| plotyly | to make the visualizations |
| gganimate | to make the plots interactive |
| RColorBrewer | to have greater control over the color in our plots |
The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.
Context
Greenhouse gas emissions are due to both natural processes and anthropogenic (human-derived) activities.
These emissions are one of the contributing factors to rising global temperatures, which can have a great influence on public health as illustrated in the following image:
Gases in the atmosphere can contribute to climate change both directly and indirectly. Direct effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the substance produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or when a gas affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or albedo). The IPCC developed the Global Warming Potential (GWP) concept to compare the ability of a greenhouse gas to trap heat in the atmosphere relative to another gas. The GWP of a greenhouse gas is defined as the ratio of the accumulated radiative forcing within a specific time horizon caused by emitting 1 kilogram of the gas, relative to that of the reference gas CO2 (IPCC 2013). Therefore GWP-weighted emissions are provided in million metric tons of CO2 equivalent (MMT CO2 Eq.)
CO2 is actually the least capable of the greenhouse gases for trapping heat:
However, because CO2 is so much more abundant and stays in the atmosphere so much longer than other greenhouse gases, it has been the largest contributor to global warming.
See here for more details.
Furthermore, sizing CO2 levels also influence ocean acidity:
This makes it difficult for organisms to maintain their shells or skeletons that are made of calcium carbonate, thus making it more difficult for these organisms to survive and impacting their role in the ecosystem and food chain.
Furthermore, greenhouse gas emissions are believed to influence storm rates.
Indeed events with high levels of precipitation which can induce flooding and property damage are generally increasing around the country:
Limitations
There are some important considerations regarding this data analysis to keep in mind:
Correlation or association does not imply causation
Limitaiton 2
What are the data?
In this case study we will be using data related to CO2 emissions, as well as other data that may influence, be influenced or relate to CO2 emissions. Most of our data was obtained from Gapminder, which is a unique nonprofit that provides a variety of data for free.
In their words, Gapminder is…
Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand. Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found here.
The data that we will be using from Gapminder was obtained from the World Bank.
In addition we will use some data that is specific to the United States from the [National Oceanic and Atmospheric Administration (NOAA)] (https://www.noaa.gov/), which is an agency that collects weather and climate data.
| Data | Source | Orginal Source | Description |
|---|---|---|---|
| CO2 emissions | Gapminder | Carbon Dioxid Information Analysis Center (CDIAC) | CO2 emissions in tonnes or metric tons (equivalent to approximately 2,204.6 pounds) per person |
| GDP per capita, yearly growth | Gapminder | World Bank | Growth Domestic Product (which is an overall measure of the health of nation’s economy) per person |
| Energy use per person | Gapminder | World Bank | Use of primary energy before transformation to other end-use fules, by country starting in 1960 |
| Crude Mortality Rate | World Bank | World Bank | Death rate per 1,000 people from 1960 to 2018 by country |
| US Natural Disasters | The National Oceanic and Atmospheric Administration (NOAA) | The National Oceanic and Atmospheric Administration (NOAA) | Data from 1980 to 2019 including: – Drought Count – Flooding Count – Freeze Count – Severe Storm Count – Tropical Cyclone Count – Wildfire Count – Winter Storm Count |
| Temperature | The National Oceanic and Atmospheric Administration (NOAA) | The National Oceanic and Atmospheric Administration (NOAA) | National yearly average temperature (in Fahrenheit) from 1895 to 2019 |
To obtain the temperature data, annual average temperatures were selected as shown in this image: 
Data Import
To read in the files that were downloaded from the various sources as indicated in the table above, we will use the read_xlsx() and read_xls() functions of the readxl package to import the data from the .xlsx and .xls files respectively and we will use the read_csv function of the readr package to import the data from the csv files.
# xlsx files:
CO2_emissions <- readxl::read_xlsx(here("docs/yearly_co2_emissions_1000_tonnes.xlsx"))
gdp_growth <- readxl::read_xlsx(here("docs/gdp_per_capita_yearly_growth.xlsx"))
energy_use <- readxl::read_xlsx(here("docs/energy_use_per_person.xlsx"))
# xls file:
mortality <- readxl::read_xls(here("docs/API_SP.DYN.CDRT.IN_DS2_en_excel_v2_804384.xls"))For our csv data files, there are some lines that we would like to not import - infact, we will get an error if we try to import them because our table structure will be as r expects. We can do so using the skip = argument of the read_csv() function.
Here you can see that the first two rows of the data about US Disasters doesn’t have the same number of columns as the subsequent rows. So we want to skip these first two lines, we will use skip = 2 for this.
Now looking at the temperature data, we can see that the first four lines do not have the same number of columns as the subsequent lines. We will skip importing all 4 lines by using skip = 4. We can also specify that NA values are encoded as "-99". This will replace all instances of "-99" with NA. We can do this using the na = argument of the read_csv() function. We will do so as: na = "-99". The “-99” needs to be in quotation markes becuase this argument expects characters.
#csv files:
us_disaster <- readr::read_csv(here("docs/time-series-US.csv"), skip = 2)
us_temperature <- readr::read_csv(here("docs/temperature.txt"), skip = 4, na ="-99")Great! now we have imported all of the data that we will need.
Data Exploration and Wrangling
Now we will take a look at our data and wrangle it until it is easy to use to allow us to evaluate how CO2 emissions have changed over time and how emissions may relate to energy use, mortality, GDP etc.
Yearly CO2 Emissions
First let’s take a look at the CO2 data. We can use the base head() function to see just the first 6 rows of our data.
We will use the %>% pipe which can be used to define the input for later sequential steps. This will make more sense when we have multiple sequential steps using the same data object. To use the pipe notation we need to install and load the dplyr package.
# A tibble: 6 x 265
country `1751` `1752` `1753` `1754` `1755` `1756` `1757` `1758` `1759` `1760`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghan… NA NA NA NA NA NA NA NA NA NA
2 Albania NA NA NA NA NA NA NA NA NA NA
3 Algeria NA NA NA NA NA NA NA NA NA NA
4 Andorra NA NA NA NA NA NA NA NA NA NA
5 Angola NA NA NA NA NA NA NA NA NA NA
6 Antigu… NA NA NA NA NA NA NA NA NA NA
# … with 254 more variables: `1761` <dbl>, `1762` <dbl>, `1763` <dbl>,
# `1764` <dbl>, `1765` <dbl>, `1766` <dbl>, `1767` <dbl>, `1768` <dbl>,
# `1769` <dbl>, `1770` <dbl>, `1771` <dbl>, `1772` <dbl>, `1773` <dbl>,
# `1774` <dbl>, `1775` <dbl>, `1776` <dbl>, `1777` <dbl>, `1778` <dbl>,
# `1779` <dbl>, `1780` <dbl>, `1781` <dbl>, `1782` <dbl>, `1783` <dbl>,
# `1784` <dbl>, `1785` <dbl>, `1786` <dbl>, `1787` <dbl>, `1788` <dbl>,
# `1789` <dbl>, `1790` <dbl>, `1791` <dbl>, `1792` <dbl>, `1793` <dbl>,
# `1794` <dbl>, `1795` <dbl>, `1796` <dbl>, `1797` <dbl>, `1798` <dbl>,
# `1799` <dbl>, `1800` <dbl>, `1801` <dbl>, `1802` <dbl>, `1803` <dbl>,
# `1804` <dbl>, `1805` <dbl>, `1806` <dbl>, `1807` <dbl>, `1808` <dbl>,
# `1809` <dbl>, `1810` <dbl>, `1811` <dbl>, `1812` <dbl>, `1813` <dbl>,
# `1814` <dbl>, `1815` <dbl>, `1816` <dbl>, `1817` <dbl>, `1818` <dbl>,
# `1819` <dbl>, `1820` <dbl>, `1821` <dbl>, `1822` <dbl>, `1823` <dbl>,
# `1824` <dbl>, `1825` <dbl>, `1826` <dbl>, `1827` <dbl>, `1828` <dbl>,
# `1829` <dbl>, `1830` <dbl>, `1831` <dbl>, `1832` <dbl>, `1833` <dbl>,
# `1834` <dbl>, `1835` <dbl>, `1836` <dbl>, `1837` <dbl>, `1838` <dbl>,
# `1839` <dbl>, `1840` <dbl>, `1841` <dbl>, `1842` <dbl>, `1843` <dbl>,
# `1844` <dbl>, `1845` <dbl>, `1846` <dbl>, `1847` <dbl>, `1848` <dbl>,
# `1849` <dbl>, `1850` <dbl>, `1851` <dbl>, `1852` <dbl>, `1853` <dbl>,
# `1854` <dbl>, `1855` <dbl>, `1856` <dbl>, `1857` <dbl>, `1858` <dbl>,
# `1859` <dbl>, `1860` <dbl>, …OK, we can see that our country data makes of the rows and the yearly data makes up the columns. We also see that we have alot of NA values.
We can also use the glimpse() function of the dplyr packge to view our data. This allows us to see more of our data at once. We will see a tiny bit of each variable/column. To do so our data will be displayed with the column names listed on the right.
Rows: 192
Columns: 265
$ country <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "An…
$ `1751` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1752` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1753` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1754` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1755` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1756` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1757` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1758` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1759` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1760` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1761` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1762` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1763` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1764` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1765` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1766` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1767` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1768` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1769` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1770` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1771` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1772` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1773` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1774` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1775` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1776` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1777` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1778` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1779` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1780` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1781` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1782` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1783` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1784` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1785` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1786` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1787` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1788` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1789` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1790` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1791` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1792` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1793` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1794` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1795` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1796` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1797` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1798` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1799` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1800` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1801` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1802` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1803` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1804` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1805` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1806` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1807` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 169, NA, NA, NA, NA, NA, …
$ `1808` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1809` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1810` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1811` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1812` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1813` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1814` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1815` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1816` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1817` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1818` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1819` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 253, NA, NA, NA, NA, NA, …
$ `1820` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 334, NA, NA, NA, NA, NA, …
$ `1821` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 359, NA, NA, NA, NA, NA, …
$ `1822` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 367, NA, NA, NA, NA, NA, …
$ `1823` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 348, NA, NA, NA, NA, NA, …
$ `1824` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 400, NA, NA, NA, NA, NA, …
$ `1825` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 403, NA, NA, NA, NA, NA, …
$ `1826` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 458, NA, NA, NA, NA, NA, …
$ `1827` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 477, NA, NA, NA, NA, NA, …
$ `1828` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 458, NA, NA, NA, NA, NA, …
$ `1829` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 477, NA, NA, NA, NA, NA, …
$ `1830` <dbl> NA, NA, NA, NA, NA, NA, NA, 0.032, NA, 495.000, 0.308, NA, NA…
$ `1831` <dbl> NA, NA, NA, NA, NA, NA, NA, 3.84e-02, NA, 4.80e+02, 3.70e-01,…
$ `1832` <dbl> NA, NA, NA, NA, NA, NA, NA, 2.56e-02, NA, 5.13e+02, 2.47e-01,…
$ `1833` <dbl> NA, NA, NA, NA, NA, NA, NA, 0.032, NA, 429.000, 0.308, NA, NA…
$ `1834` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 587, NA, NA, NA, NA, NA, …
$ `1835` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 634, NA, NA, NA, NA, NA, …
$ `1836` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 675, NA, NA, NA, NA, NA, …
$ `1837` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 708, NA, NA, NA, NA, NA, …
$ `1838` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 851, NA, NA, NA, NA, NA, …
$ `1839` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1060, NA, NA, NA, NA, NA,…
$ `1840` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1170, NA, NA, NA, NA, NA,…
$ `1841` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1320, NA, NA, NA, NA, NA,…
$ `1842` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1460, NA, NA, NA, NA, NA,…
$ `1843` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1270, NA, NA, NA, NA, NA,…
$ `1844` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1600, NA, NA, NA, NA, NA,…
$ `1845` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1800, NA, NA, NA, NA, NA,…
$ `1846` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2120, NA, NA, NA, NA, NA,…
$ `1847` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2080, NA, NA, NA, NA, NA,…
$ `1848` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2340, NA, NA, NA, NA, NA,…
$ `1849` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2260, NA, NA, NA, NA, NA,…
$ `1850` <dbl> NA, NA, NA, NA, NA, NA, NA, 0.198, NA, 2330.000, 1.910, NA, N…
$ `1851` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2340, NA, NA, NA, NA, NA,…
$ `1852` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2810, NA, NA, NA, NA, NA,…
$ `1853` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 3230, NA, NA, NA, NA, NA,…
$ `1854` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 3180, NA, NA, NA, NA, NA,…
$ `1855` <dbl> NA, NA, NA, NA, NA, NA, NA, 6.01e-01, NA, 3.70e+03, 5.80e+00,…
$ `1856` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 4240, NA, NA, NA, NA, NA,…
$ `1857` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 4880, NA, NA, NA, NA, NA,…
$ `1858` <dbl> NA, NA, NA, NA, NA, NA, NA, 8.44e-01, NA, 7.25e+03, 8.14e+00,…
$ `1859` <dbl> NA, NA, NA, NA, NA, NA, NA, 8.95e-01, NA, 5.87e+03, 8.64e+00,…
$ `1860` <dbl> NA, NA, NA, NA, NA, NA, NA, 1.18, 279.00, 6150.00, 11.40, NA,…
$ `1861` <dbl> NA, NA, NA, NA, NA, NA, NA, 1.5, 510.0, 6380.0, 14.5, NA, NA,…
$ `1862` <dbl> NA, NA, NA, NA, NA, NA, NA, 1.36, 356.00, 6360.00, 13.10, NA,…
$ `1863` <dbl> NA, NA, NA, NA, NA, NA, NA, 1.42, 400.00, 5880.00, 13.70, NA,…
$ `1864` <dbl> NA, NA, NA, NA, NA, NA, NA, 1.59, 268.00, 5080.00, 15.40, NA,…
$ `1865` <dbl> NA, NA, NA, NA, NA, NA, NA, 1.52, 422.00, 5360.00, 14.70, NA,…
$ `1866` <dbl> NA, NA, NA, NA, NA, NA, NA, 4.81, 697.00, 3600.00, 46.40, NA,…
$ `1867` <dbl> NA, NA, NA, NA, NA, NA, NA, 5.52, 895.00, 4920.00, 53.20, NA,…
$ `1868` <dbl> NA, NA, NA, NA, NA, NA, NA, 4.59, 733.00, 6080.00, 44.30, NA,…
$ `1869` <dbl> NA, NA, NA, NA, NA, NA, NA, 6.23, 642.00, 6490.00, 60.10, NA,…
$ `1870` <dbl> NA, NA, NA, NA, NA, NA, NA, 6.76, 601.00, 7370.00, 65.20, NA,…
$ `1871` <dbl> NA, NA, NA, NA, NA, NA, NA, 9.12, 693.00, 10200.00, 88.00, NA…
$ `1872` <dbl> NA, NA, NA, NA, NA, NA, NA, 9.36, 708.00, 10000.00, 90.40, NA…
$ `1873` <dbl> NA, NA, NA, NA, NA, NA, NA, 8.79, 869.00, 10700.00, 84.80, NA…
$ `1874` <dbl> NA, NA, NA, NA, NA, NA, NA, 10.7, 891.0, 9160.0, 103.0, NA, N…
$ `1875` <dbl> NA, NA, NA, NA, NA, NA, NA, 12.3, 829.0, 7870.0, 119.0, NA, N…
$ `1876` <dbl> NA, NA, NA, NA, NA, NA, NA, 15.2, 931.0, 8100.0, 147.0, NA, N…
$ `1877` <dbl> NA, NA, NA, NA, NA, NA, NA, 15.6, 1070.0, 7290.0, 150.0, NA, …
$ `1878` <dbl> NA, NA, NA, NA, NA, NA, NA, 20.3, 968.0, 7250.0, 196.0, NA, N…
$ `1879` <dbl> NA, NA, NA, NA, NA, NA, NA, 20.9, 1460.0, 8870.0, 201.0, NA, …
$ `1880` <dbl> NA, NA, NA, NA, NA, NA, NA, 24.5, 2210.0, 23700.0, 236.0, NA,…
$ `1881` <dbl> NA, NA, NA, NA, NA, NA, NA, 25.80, 1770.00, 10300.00, 249.00,…
$ `1882` <dbl> NA, NA, NA, NA, NA, NA, NA, 27.20, 2010.00, 10600.00, 262.00,…
$ `1883` <dbl> NA, NA, NA, NA, NA, NA, NA, 30.90, 2430.00, 11800.00, 298.00,…
$ `1884` <dbl> NA, NA, NA, NA, NA, NA, NA, 31.4, 2570.0, 11500.0, 303.0, NA,…
$ `1885` <dbl> NA, NA, NA, NA, NA, NA, NA, 34.20, 2910.00, 12100.00, 330.00,…
$ `1886` <dbl> NA, NA, NA, NA, NA, NA, NA, 35.10, 2890.00, 11400.00, 338.00,…
$ `1887` <dbl> NA, NA, NA, NA, NA, NA, 1090.0, 37.1, 3040.0, 12300.0, 358.0,…
$ `1888` <dbl> NA, NA, NA, NA, NA, NA, 891.0, 38.7, 3530.0, 12000.0, 373.0, …
$ `1889` <dbl> NA, NA, NA, NA, NA, NA, 1760.0, 41.8, 3430.0, 12900.0, 403.0,…
$ `1890` <dbl> NA, NA, NA, NA, NA, NA, 1370.0, 47.3, 3550.0, 13000.0, 457.0,…
$ `1891` <dbl> NA, NA, NA, NA, NA, NA, 939.0, 52.1, 4010.0, 15000.0, 503.0, …
$ `1892` <dbl> NA, NA, NA, NA, NA, NA, 1390.0, 55.1, 4150.0, 14500.0, 532.0,…
$ `1893` <dbl> NA, NA, NA, NA, NA, NA, 1550.0, 64.6, 3970.0, 17700.0, 624.0,…
$ `1894` <dbl> NA, NA, NA, NA, NA, NA, 1990.0, 65.8, 4360.0, 18100.0, 635.0,…
$ `1895` <dbl> NA, NA, NA, NA, NA, NA, 2270.0, 75.6, 4590.0, 20400.0, 730.0,…
$ `1896` <dbl> NA, NA, NA, NA, NA, NA, 2310, 77, 4510, 21300, 743, NA, NA, N…
$ `1897` <dbl> NA, NA, NA, NA, NA, NA, 2080, 89, 4980, 23000, 859, NA, NA, N…
$ `1898` <dbl> NA, NA, NA, NA, NA, NA, 2350.0, 99.9, 5620.0, 24500.0, 964.0,…
$ `1899` <dbl> NA, NA, NA, NA, NA, NA, 2920, 116, 5790, 24800, 1120, NA, NA,…
$ `1900` <dbl> NA, NA, NA, NA, NA, NA, 2070, 131, 10200, 27700, 1270, NA, NA…
$ `1901` <dbl> NA, NA, NA, NA, NA, NA, 2490, 135, 11400, 28400, 1300, NA, NA…
$ `1902` <dbl> NA, NA, NA, NA, NA, NA, 2820, 130, 11400, 25700, 1260, NA, NA…
$ `1903` <dbl> NA, NA, NA, NA, NA, NA, 2860, 127, 11200, 25600, 1230, NA, NA…
$ `1904` <dbl> NA, NA, NA, NA, NA, NA, 3800, 142, 11600, 26900, 1370, NA, NA…
$ `1905` <dbl> NA, NA, NA, NA, NA, NA, 3990, 126, 12100, 28100, 1220, NA, NA…
$ `1906` <dbl> NA, NA, NA, NA, NA, NA, 6260, 144, 14400, 33600, 1390, NA, NA…
$ `1907` <dbl> NA, NA, NA, NA, NA, NA, 6260, 161, 15500, 42200, 1560, NA, NA…
$ `1908` <dbl> NA, NA, NA, NA, NA, NA, 7620, 162, 16800, 59000, 1570, NA, NA…
$ `1909` <dbl> NA, NA, NA, NA, NA, NA, 5940, 172, 14600, 42200, 1660, NA, NA…
$ `1910` <dbl> NA, NA, NA, NA, NA, NA, 8910, 168, 17500, 57600, 1620, NA, NA…
$ `1911` <dbl> NA, NA, NA, NA, NA, NA, 9950, 174, 19300, 48100, 1680, NA, NA…
$ `1912` <dbl> NA, NA, NA, NA, NA, NA, 9490, 198, 20800, 50000, 1910, NA, NA…
$ `1913` <dbl> NA, NA, NA, NA, NA, NA, 10200, 215, 22400, 59700, 2070, NA, N…
$ `1914` <dbl> NA, NA, NA, NA, NA, NA, 8680, 194, 24500, 48900, 1870, NA, NA…
$ `1915` <dbl> NA, NA, NA, NA, NA, NA, 6950, 178, 21800, 34900, 1720, NA, NA…
$ `1916` <dbl> NA, NA, 3.67, NA, NA, NA, 4990.00, 189.00, 19300.00, 8040.00,…
$ `1917` <dbl> NA, NA, 7.33, NA, NA, NA, 2230.00, 174.00, 20800.00, 3450.00,…
$ `1918` <dbl> NA, NA, 18.3, NA, NA, NA, 2520.0, 69.5, 23000.0, 3340.0, 671.…
$ `1919` <dbl> NA, NA, 18.3, NA, NA, NA, 3730.0, 59.4, 21800.0, 3020.0, 573.…
$ `1920` <dbl> NA, NA, 22.0, NA, NA, NA, 5900.0, 54.2, 25800.0, 14500.0, 523…
$ `1921` <dbl> NA, NA, 25.7, NA, NA, NA, 5540.0, 58.7, 23200.0, 19400.0, 567…
$ `1922` <dbl> NA, NA, 25.7, NA, NA, NA, 7300.0, 71.6, 24400.0, 18600.0, 692…
$ `1923` <dbl> NA, NA, 14.7, NA, NA, NA, 8450.0, 79.1, 24900.0, 17800.0, 764…
$ `1924` <dbl> NA, NA, 29.3, NA, NA, NA, 11000.0, 94.3, 27100.0, 20100.0, 91…
$ `1925` <dbl> NA, NA, 33.0, NA, NA, NA, 11200.0, 93.1, 28300.0, 19000.0, 89…
$ `1926` <dbl> NA, NA, 40.3, NA, NA, NA, 11300.0, 135.0, 27900.0, 18600.0, 1…
$ `1927` <dbl> NA, NA, 58.7, NA, NA, NA, 13400.0, 168.0, 28900.0, 20100.0, 1…
$ `1928` <dbl> NA, NA, 73.30, NA, NA, NA, 12800.00, 186.00, 26300.00, 21200.…
$ `1929` <dbl> NA, NA, 80.70, NA, NA, NA, 13100.00, 201.00, 23700.00, 24200.…
$ `1930` <dbl> NA, NA, 84.30, NA, NA, NA, 12800.00, 273.00, 22000.00, 18900.…
$ `1931` <dbl> NA, NA, 99.00, NA, NA, NA, 12900.00, 328.00, 19600.00, 18100.…
$ `1932` <dbl> NA, NA, 114.00, NA, NA, NA, 13100.00, 369.00, 20400.00, 15200…
$ `1933` <dbl> NA, 7.33, 121.00, NA, NA, NA, 13200.00, 412.00, 21600.00, 142…
$ `1934` <dbl> NA, 7.33, 139.00, NA, NA, NA, 14300.00, 499.00, 22700.00, 138…
$ `1935` <dbl> NA, 18.3, 132.0, NA, NA, NA, 14000.0, 565.0, 25300.0, 13900.0…
$ `1936` <dbl> NA, 128.0, 51.3, NA, NA, NA, 15100.0, 648.0, 27100.0, 13600.0…
$ `1937` <dbl> NA, 297.0, 69.7, NA, NA, NA, 16700.0, 662.0, 28900.0, 15300.0…
$ `1938` <dbl> NA, 348, 33, NA, NA, NA, 16400, 699, 28100, 5790, 6750, NA, 3…
$ `1939` <dbl> NA, 433.00, 161.00, NA, NA, NA, 17400.00, 707.00, 32200.00, 6…
$ `1940` <dbl> NA, 693, 238, NA, NA, NA, 15900, 848, 29100, 7350, 8190, NA, …
$ `1941` <dbl> NA, 627, 312, NA, NA, NA, 14000, 745, 34600, 7980, 7190, NA, …
$ `1942` <dbl> NA, 744, 499, NA, NA, NA, 13500, 513, 36500, 8560, 4950, NA, …
$ `1943` <dbl> NA, 462, 469, NA, NA, NA, 14100, 655, 35000, 9620, 6320, NA, …
$ `1944` <dbl> NA, 154, 499, NA, NA, NA, 14000, 613, 34200, 9400, 5920, NA, …
$ `1945` <dbl> NA, 121, 616, NA, NA, NA, 13700, 649, 32700, 4570, 6270, NA, …
$ `1946` <dbl> NA, 484, 763, NA, NA, NA, 13700, 730, 35500, 12800, 7040, NA,…
$ `1947` <dbl> NA, 928.00, 744.00, NA, NA, NA, 14500.00, 878.00, 38000.00, 1…
$ `1948` <dbl> NA, 704.00, 803.00, NA, NA, NA, 17400.00, 935.00, 38500.00, 2…
$ `1949` <dbl> 14.70, 1020.00, 909.00, NA, NA, NA, 15400.00, 1060.00, 37700.…
$ `1950` <dbl> 84.3, 297.0, 3790.0, NA, 187.0, NA, 30000.0, 1180.0, 54800.0,…
$ `1951` <dbl> 91.7, 403.0, 4140.0, NA, 249.0, NA, 35000.0, 1280.0, 59100.0,…
$ `1952` <dbl> 91.7, 374.0, 3890.0, NA, 312.0, NA, 36100.0, 1370.0, 60300.0,…
$ `1953` <dbl> 106.0, 414.0, 4000.0, NA, 275.0, NA, 35200.0, 1450.0, 59500.0…
$ `1954` <dbl> 106.0, 502.0, 4160.0, NA, 348.0, NA, 36800.0, 1590.0, 67900.0…
$ `1955` <dbl> 154.0, 664.0, 4610.0, NA, 414.0, NA, 39600.0, 1800.0, 70700.0…
$ `1956` <dbl> 183.0, 840.0, 5000.0, NA, 502.0, NA, 44300.0, 1970.0, 73100.0…
$ `1957` <dbl> 293.0, 1510.0, 5540.0, NA, 620.0, 22.0, 47700.0, 2160.0, 7460…
$ `1958` <dbl> 330.0, 1200.0, 5220.0, NA, 594.0, 29.3, 44200.0, 2310.0, 7770…
$ `1959` <dbl> 385.0, 1440.0, 5670.0, NA, 620.0, 29.3, 49000.0, 2430.0, 8380…
$ `1960` <dbl> 414.0, 2020.0, 6160.0, NA, 550.0, 36.7, 48800.0, 2530.0, 8820…
$ `1961` <dbl> 491.0, 2280.0, 6070.0, NA, 455.0, 47.7, 51200.0, 2600.0, 9060…
$ `1962` <dbl> 689.0, 2460.0, 5670.0, NA, 1180.0, 103.0, 53700.0, 2730.0, 94…
$ `1963` <dbl> 708.0, 2080.0, 5430.0, NA, 1150.0, 84.3, 50100.0, 2930.0, 101…
$ `1964` <dbl> 840.0, 2020.0, 5650.0, NA, 1220.0, 91.7, 55700.0, 3120.0, 109…
$ `1965` <dbl> 1010.0, 2170.0, 6600.0, NA, 1190.0, 150.0, 58900.0, 3310.0, 1…
$ `1966` <dbl> 1090.0, 2550.0, 8430.0, NA, 1550.0, 348.0, 63100.0, 3490.0, 1…
$ `1967` <dbl> 1280, 2680, 8440, NA, 994, 565, 65500, 3650, 129000, 40000, 3…
$ `1968` <dbl> 1220, 3070, 9060, NA, 1670, 990, 69100, 3750, 135000, 42400, …
$ `1969` <dbl> 942, 3250, 11300, NA, 2790, 1260, 77300, 3910, 142000, 44700,…
$ `1970` <dbl> 1.67e+03, 3.74e+03, 1.51e+04, NA, 3.58e+03, 4.62e+02, 8.27e+0…
$ `1971` <dbl> 1.90e+03, 4.35e+03, 1.87e+04, NA, 3.41e+03, 4.25e+02, 8.89e+0…
$ `1972` <dbl> 1.53e+03, 5.64e+03, 2.83e+04, NA, 4.51e+03, 3.74e+02, 9.02e+0…
$ `1973` <dbl> 1.64e+03, 5.29e+03, 3.83e+04, NA, 4.88e+03, 3.30e+02, 9.41e+0…
$ `1974` <dbl> 1.92e+03, 4.35e+03, 3.19e+04, NA, 4.87e+03, 4.29e+02, 9.56e+0…
$ `1975` <dbl> 2.13e+03, 4.59e+03, 3.20e+04, NA, 4.42e+03, 7.08e+02, 9.49e+0…
$ `1976` <dbl> 1.99e+03, 4.95e+03, 3.92e+04, NA, 3.29e+03, 4.03e+02, 9.98e+0…
$ `1977` <dbl> 2.39e+03, 5.72e+03, 4.19e+04, NA, 3.53e+03, 4.66e+02, 1.01e+0…
$ `1978` <dbl> 2160, 6490, 62500, NA, 5410, 491, 103000, 5810, 202000, 57500…
$ `1979` <dbl> 2240, 7590, 45600, NA, 5500, 407, 111000, 5850, 205000, 61600…
$ `1980` <dbl> 1760, 5170, 66500, NA, 5350, 143, 109000, 6080, 221000, 52300…
$ `1981` <dbl> 1980.0, 7340.0, 46400.0, NA, 5280.0, 106.0, 102000.0, 5970.0,…
$ `1982` <dbl> 2100, 7310, 39300, NA, 4650, 293, 103000, 6080, 234000, 53900…
$ `1983` <dbl> 2520.0, 7630.0, 52600.0, NA, 5120.0, 84.3, 105000.0, 6170.0, …
$ `1984` <dbl> 2830.0, 7830.0, 71100.0, NA, 5010.0, 147.0, 107000.0, 6230.0,…
$ `1985` <dbl> 3510.0, 7880.0, 72800.0, NA, 4700.0, 249.0, 101000.0, 6710.0,…
$ `1986` <dbl> 3140, 8060, 76300, NA, 4660, 249, 104000, 6730, 240000, 54100…
$ `1987` <dbl> 3120, 7440, 84100, NA, 5820, 275, 115000, 7020, 256000, 57700…
$ `1988` <dbl> 2870, 7330, 83900, NA, 5130, 286, 121000, 7210, 261000, 53300…
$ `1989` <dbl> 2780.0, 8980.0, 80000.0, NA, 5010.0, 286.0, 117000.0, 7060.0,…
$ `1990` <dbl> 2610, 5520, 77000, 407, 5120, 282, 112000, 6620, 264000, 5770…
$ `1991` <dbl> 2440, 4290, 79000, 407, 5090, 268, 117000, 6380, 261000, 6160…
$ `1992` <dbl> 1390, 2520, 80100, 407, 5200, 264, 121000, 5830, 268000, 5670…
$ `1993` <dbl> 1350, 2340, 82200, 411, 5780, 271, 118000, 2560, 277000, 5710…
$ `1994` <dbl> 1290, 1930, 86400, 407, 3890, 268, 122000, 2710, 278000, 5710…
$ `1995` <dbl> 1240, 2090, 95300, 425, 11000, 275, 128000, 3410, 282000, 598…
$ `1996` <dbl> 1180, 2020, 97100, 455, 10500, 293, 135000, 2560, 302000, 632…
$ `1997` <dbl> 1100, 1540, 87300, 466, 7380, 308, 138000, 3230, 306000, 6270…
$ `1998` <dbl> 1040, 1750, 107000, 491, 7310, 319, 140000, 3360, 317000, 637…
$ `1999` <dbl> 821, 2980, 92000, 513, 9160, 330, 147000, 3010, 325000, 61900…
$ `2000` <dbl> 774, 3020, 87900, 524, 9540, 345, 142000, 3470, 329000, 62300…
$ `2001` <dbl> 818, 3220, 84200, 524, 9730, 348, 134000, 3540, 325000, 65900…
$ `2002` <dbl> 1070, 3750, 89900, 532, 12700, 370, 125000, 3040, 341000, 671…
$ `2003` <dbl> 1200, 4290, 91600, 535, 9060, 403, 135000, 3430, 336000, 7220…
$ `2004` <dbl> 950, 4170, 88500, 561, 18800, 422, 158000, 3640, 343000, 7240…
$ `2005` <dbl> 1330, 4250, 107000, 576, 19200, 429, 162000, 4350, 350000, 74…
$ `2006` <dbl> 1650, 3900, 101000, 546, 22300, 444, 175000, 4380, 365000, 72…
$ `2007` <dbl> 2270, 3930, 109000, 539, 25200, 469, 175000, 5060, 372000, 69…
$ `2008` <dbl> 4210, 4370, 110000, 539, 25700, 480, 189000, 5560, 386000, 69…
$ `2009` <dbl> 6770, 4380, 121000, 517, 27800, 510, 180000, 4360, 395000, 62…
$ `2010` <dbl> 8460, 4600, 119000, 517, 29100, 524, 188000, 4220, 391000, 67…
$ `2011` <dbl> 12200, 5240, 121000, 491, 30300, 513, 192000, 4920, 392000, 6…
$ `2012` <dbl> 10800, 4910, 130000, 488, 33400, 524, 192000, 5690, 388000, 6…
$ `2013` <dbl> 10000, 5060, 134000, 477, 32600, 524, 190000, 5500, 372000, 6…
$ `2014` <dbl> 9810, 5720, 145000, 462, 34800, 532, 204000, 5530, 361000, 58…We can see that we have a large tibble. A tibble is the tidyverse version of a data frame. It is essentially a table with variable information arranged as columns, and individual observations arranged as rows. We can see that the tibble gives us information about the class of each variable. For example the country variable is made up of character (abbreviated as chr) values. We see that we have 265 different country variables and CO2 emission values for 192 different years (from 1751 to 2014). Recall that the values are emissions in metric tons also called tonnes. We can see that there are fewer NA values for later years.
Now we will modify this data to make it more usable for making visualizations. One thing we will use is the %<>% opperator which is from the magrittr package. This allows us to use our CO2_emissions data and reassign it to a modified version at the same time.
We will use the pivot_longer() function of the dplyr package to convert our data into what is called long format. This means that we will have more rows and fewer columns than our current format. This is done by collapsing multiple variables into fewer variables.
We want to collapse all of the values for the emission data across the different individual year variables into one new emission variable and we will identify what year they are from using a new Year variable.
CO2_emissions %<>%
pivot_longer(cols = -country, names_to = "Year", values_to = "Emissions")
head(CO2_emissions)# A tibble: 6 x 3
country Year Emissions
<chr> <chr> <dbl>
1 Afghanistan 1751 NA
2 Afghanistan 1752 NA
3 Afghanistan 1753 NA
4 Afghanistan 1754 NA
5 Afghanistan 1755 NA
6 Afghanistan 1756 NAWe also want to rename the country variable to be capitalized. W We can use the rename() function of the dplyr package to rename this variable. When renaming variables the new name is listed first before the =. We will also modify the Emissions data by dividing it by 1000 to make the numbers smaller. To do this we will use the mutate() function, which is also part of the dplyr() package. This function allows us to create and modify variables. You may also note that the Year variable is currently of class type character. We would like to change it to be numeric. This can also be accomplished using the mutate() function.
CO2_emissions %<>%
dplyr::rename(Country=country) %>%
dplyr::mutate(Emissions = Emissions/1000,
Year = as.numeric(Year)) %>%
rename(`CO2 Emissions (Mg)`= Emissions)Now let’s take a look to see how our data has changed:
# A tibble: 6 x 3
Country Year `CO2 Emissions (Mg)`
<chr> <dbl> <dbl>
1 Afghanistan 1751 NA
2 Afghanistan 1752 NA
3 Afghanistan 1753 NA
4 Afghanistan 1754 NA
5 Afghanistan 1755 NA
6 Afghanistan 1756 NAGreat, we can see that now the Year variable is of class double (abbreviated dbl), which is a numeric class.
Yearly Growth in GDP per Capita
# A tibble: 6 x 220
country `1801` `1802` `1803` `1804` `1805` `1806` `1807`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghan… NA NA NA NA NA NA NA
2 Albania 0.104 0.104 0.104 0.104 0.104 0.104 0.104
3 Algeria -0.00247 -0.00247 -0.00247 -0.00247 -0.00247 -0.00247 -0.00247
4 Andorra 0.166 0.166 0.166 0.166 0.166 0.166 0.166
5 Angola 0.425 0.425 0.425 0.425 0.425 0.425 0.425
6 Antigu… NA NA NA NA NA NA NA
# … with 212 more variables: `1808` <dbl>, `1809` <dbl>, `1810` <dbl>,
# `1811` <dbl>, `1812` <dbl>, `1813` <dbl>, `1814` <dbl>, `1815` <dbl>,
# `1816` <dbl>, `1817` <dbl>, `1818` <dbl>, `1819` <dbl>, `1820` <dbl>,
# `1821` <dbl>, `1822` <dbl>, `1823` <dbl>, `1824` <dbl>, `1825` <dbl>,
# `1826` <dbl>, `1827` <dbl>, `1828` <dbl>, `1829` <dbl>, `1830` <dbl>,
# `1831` <dbl>, `1832` <dbl>, `1833` <dbl>, `1834` <dbl>, `1835` <dbl>,
# `1836` <dbl>, `1837` <dbl>, `1838` <dbl>, `1839` <dbl>, `1840` <dbl>,
# `1841` <dbl>, `1842` <dbl>, `1843` <dbl>, `1844` <dbl>, `1845` <dbl>,
# `1846` <dbl>, `1847` <dbl>, `1848` <dbl>, `1849` <dbl>, `1850` <dbl>,
# `1851` <dbl>, `1852` <dbl>, `1853` <dbl>, `1854` <dbl>, `1855` <dbl>,
# `1856` <dbl>, `1857` <dbl>, `1858` <dbl>, `1859` <dbl>, `1860` <dbl>,
# `1861` <dbl>, `1862` <dbl>, `1863` <dbl>, `1864` <dbl>, `1865` <dbl>,
# `1866` <dbl>, `1867` <dbl>, `1868` <dbl>, `1869` <dbl>, `1870` <dbl>,
# `1871` <dbl>, `1872` <dbl>, `1873` <dbl>, `1874` <dbl>, `1875` <dbl>,
# `1876` <dbl>, `1877` <dbl>, `1878` <dbl>, `1879` <dbl>, `1880` <dbl>,
# `1881` <dbl>, `1882` <dbl>, `1883` <dbl>, `1884` <dbl>, `1885` <dbl>,
# `1886` <dbl>, `1887` <dbl>, `1888` <dbl>, `1889` <dbl>, `1890` <dbl>,
# `1891` <dbl>, `1892` <dbl>, `1893` <dbl>, `1894` <dbl>, `1895` <dbl>,
# `1896` <dbl>, `1897` <dbl>, `1898` <dbl>, `1899` <dbl>, `1900` <dbl>,
# `1901` <dbl>, `1902` <dbl>, `1903` <dbl>, `1904` <dbl>, `1905` <dbl>,
# `1906` <dbl>, `1907` <dbl>, … [1] "country" "1801" "1802" "1803" "1804" "1805" "1806"
[8] "1807" "1808" "1809" "1810" "1811" "1812" "1813"
[15] "1814" "1815" "1816" "1817" "1818" "1819" "1820"
[22] "1821" "1822" "1823" "1824" "1825" "1826" "1827"
[29] "1828" "1829" "1830" "1831" "1832" "1833" "1834"
[36] "1835" "1836" "1837" "1838" "1839" "1840" "1841"
[43] "1842" "1843" "1844" "1845" "1846" "1847" "1848"
[50] "1849" "1850" "1851" "1852" "1853" "1854" "1855"
[57] "1856" "1857" "1858" "1859" "1860" "1861" "1862"
[64] "1863" "1864" "1865" "1866" "1867" "1868" "1869"
[71] "1870" "1871" "1872" "1873" "1874" "1875" "1876"
[78] "1877" "1878" "1879" "1880" "1881" "1882" "1883"
[85] "1884" "1885" "1886" "1887" "1888" "1889" "1890"
[92] "1891" "1892" "1893" "1894" "1895" "1896" "1897"
[99] "1898" "1899" "1900" "1901" "1902" "1903" "1904"
[106] "1905" "1906" "1907" "1908" "1909" "1910" "1911"
[113] "1912" "1913" "1914" "1915" "1916" "1917" "1918"
[120] "1919" "1920" "1921" "1922" "1923" "1924" "1925"
[127] "1926" "1927" "1928" "1929" "1930" "1931" "1932"
[134] "1933" "1934" "1935" "1936" "1937" "1938" "1939"
[141] "1940" "1941" "1942" "1943" "1944" "1945" "1946"
[148] "1947" "1948" "1949" "1950" "1951" "1952" "1953"
[155] "1954" "1955" "1956" "1957" "1958" "1959" "1960"
[162] "1961" "1962" "1963" "1964" "1965" "1966" "1967"
[169] "1968" "1969" "1970" "1971" "1972" "1973" "1974"
[176] "1975" "1976" "1977" "1978" "1979" "1980" "1981"
[183] "1982" "1983" "1984" "1985" "1986" "1987" "1988"
[190] "1989" "1990" "1991" "1992" "1993" "1994" "1995"
[197] "1996" "1997" "1998" "1999" "2000" "2001" "2002"
[204] "2003" "2004" "2005" "2006" "2007" "2008" "2009"
[211] "2010" "2011" "2012" "2013" "2014" "2015" "2016"
[218] "2017" "2018" "2019" Rows: 194
Columns: 220
$ country <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "An…
$ `1801` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1802` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1803` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1804` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1805` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1806` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1807` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1808` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1809` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1810` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1811` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1812` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1813` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1814` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1815` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1816` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1817` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1818` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1819` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1820` <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1821` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1822` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1823` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1824` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1825` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1826` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1827` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1828` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1829` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1830` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1831` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1832` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1833` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1834` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1835` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1836` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1837` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1838` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1839` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1840` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1841` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1842` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1843` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1844` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1845` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1846` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1847` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1848` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1849` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1850` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1851` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1852` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1853` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1854` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1855` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1856` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1857` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1858` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1859` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1860` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1861` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1862` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1863` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1864` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1865` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1866` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1867` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1868` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1869` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1870` <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1871` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 0.772…
$ `1872` <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 1.4100, 0.371…
$ `1873` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 7.600…
$ `1874` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 0.292…
$ `1875` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 7.910…
$ `1876` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -0.952, 0.371, -3.1…
$ `1877` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 7.210, 0.371, 0.720…
$ `1878` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -8.110, 0.371, 5.98…
$ `1879` <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 1.2700, 0.371…
$ `1880` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -5.020, 0.371, 1.91…
$ `1881` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -1.500, 0.371, 3.95…
$ `1882` <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 22.8000, 0.37…
$ `1883` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 9.000, 0.371, 10.20…
$ `1884` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 3.980, 0.371, -3.84…
$ `1885` <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 14.2000, 0.37…
$ `1886` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -2.700, -4.080, -2.…
$ `1887` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 3.690, 16.700, 6.98…
$ `1888` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 12.900, -4.020, -2.…
$ `1889` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 6.590, -7.220, 5.40…
$ `1890` <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -11.300, -0.635, -6…
$ `1891` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, -8.460, -8.610, 4.6…
$ `1892` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 16.300, 9.230, -14.…
$ `1893` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 2.840, 13.100, -7.2…
$ `1894` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 12.200, 13.400, 1.5…
$ `1895` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 7.760, -7.670, -7.3…
$ `1896` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 7.520, 9.890, 5.670…
$ `1897` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, -21.900, -1.890, -7…
$ `1898` <dbl> 0.3250, 1.3700, 1.1400, 1.1700, 0.4250, 0.6610, 5.4600, 2.430…
$ `1899` <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 14.700, 5.880, -1.3…
$ `1900` <dbl> 0.3250, 1.3700, 1.1400, 1.1700, 0.4250, 0.6610, -14.8000, -2.…
$ `1901` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 5.620, 2.270, -4.35…
$ `1902` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, -4.850, 8.420, -0.4…
$ `1903` <dbl> 0.3250, 1.3100, 1.1400, 1.1700, 0.4250, 0.6610, 11.5000, -7.1…
$ `1904` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 7.830, 10.300, 5.36…
$ `1905` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 10.400, -11.700, -0…
$ `1906` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 0.392, -4.640, 5.26…
$ `1907` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, -2.530, -4.130, 2.4…
$ `1908` <dbl> 0.3250, 1.3100, 1.1400, 1.1700, 0.4250, 0.6610, 5.1600, 8.960…
$ `1909` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 0.294, 3.590, 6.130…
$ `1910` <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 2.640, 6.460, 4.600…
$ `1911` <dbl> 0.32500, 1.28000, 1.14000, 1.17000, 0.42500, 0.66100, -2.2000…
$ `1912` <dbl> 0.32500, 1.28000, 1.14000, 1.17000, 0.42500, 0.66100, 4.17000…
$ `1913` <dbl> 0.32500, 1.28000, 1.14000, 1.17000, 0.42500, 0.66100, -2.9600…
$ `1914` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -14.300, -4.720, -2…
$ `1915` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -3.470, 2.690, -2.5…
$ `1916` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -4.610, -3.150, -0.…
$ `1917` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -9.830, -16.700, -1…
$ `1918` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 16.600, -16.700, -3…
$ `1919` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 1.950, -16.700, 2.4…
$ `1920` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 5.550, -5.070, 1.06…
$ `1921` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -0.488, -5.070, 3.0…
$ `1922` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 4.950, 8.450, 3.110…
$ `1923` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 7.970, 8.450, 2.540…
$ `1924` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 4.750, 8.450, 4.340…
$ `1925` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -3.460, 8.450, 2.50…
$ `1926` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 2.080, 8.450, 0.351…
$ `1927` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 4.350, 8.450, -0.51…
$ `1928` <dbl> 0.46300, 0.83700, 0.43200, 3.80000, 2.96000, 2.45000, 3.45000…
$ `1929` <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 1.860, 8.450, -3.46…
$ `1930` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -6.880, 4.280, -10.…
$ `1931` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -8.810, 0.680, -7.5…
$ `1932` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -5.180, -1.760, 4.8…
$ `1933` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 2.830, 3.530, 6.110…
$ `1934` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 6.020, 8.930, 4.490…
$ `1935` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 2.480, 14.100, 5.09…
$ `1936` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -0.737, 6.560, 3.73…
$ `1937` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 5.680, 8.050, 4.170…
$ `1938` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -1.260, -0.505, 2.4…
$ `1939` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 2.260, 3.820, -0.69…
$ `1940` <dbl> 0.4630, 0.3720, 0.4320, 3.8000, 2.9600, 2.4500, 0.0522, -4.38…
$ `1941` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 3.670, -2.100, 10.1…
$ `1942` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -0.457, -2.100, 10.…
$ `1943` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -2.240, -2.100, 2.6…
$ `1944` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 9.710, -2.100, -4.4…
$ `1945` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -4.770, -2.100, -6.…
$ `1946` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 6.610, -2.100, -4.6…
$ `1947` <dbl> 0.4630, 0.3720, 0.4320, 3.8000, 2.9600, 2.4500, 8.8000, 10.90…
$ `1948` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 3.160, 12.700, 4.55…
$ `1949` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -3.620, 8.980, 3.87…
$ `1950` <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -1.110, 8.070, 2.42…
$ `1951` <dbl> 1.250, 4.320, -1.300, 3.800, 2.320, 2.450, 1.750, -1.470, 1.2…
$ `1952` <dbl> 1.660, 0.160, 2.150, 3.800, 2.320, 2.450, -7.090, 4.430, -1.1…
$ `1953` <dbl> 4.290, 4.040, -0.517, 3.800, 2.220, 2.450, 3.320, 2.360, 1.18…
$ `1954` <dbl> 0.3080, 2.9400, 4.9900, 3.8000, -4.1100, 2.4500, 2.2100, 2.87…
$ `1955` <dbl> 0.129, 5.390, 0.573, 3.800, 6.410, 2.450, 5.200, 6.420, 3.040…
$ `1956` <dbl> 2.530, 1.010, 7.460, 3.800, -3.350, 2.450, 0.962, 7.410, 1.01…
$ `1957` <dbl> -1.9400, 6.4100, 9.0300, 3.8000, 7.8600, 2.4500, 3.4200, 0.04…
$ `1958` <dbl> 3.520, 4.500, 1.510, 3.800, 3.620, 2.450, 4.390, 5.390, 2.660…
$ `1959` <dbl> 0.764, 4.120, 16.000, 3.800, -1.230, 2.450, -8.140, -3.080, 3…
$ `1960` <dbl> 1.430, 5.060, 4.750, 3.800, 2.260, 2.450, 6.220, 7.290, 1.880…
$ `1961` <dbl> -1.280, 0.831, -13.800, 3.800, 11.400, 2.450, 5.480, 3.870, -…
$ `1962` <dbl> -0.497, 3.310, -20.400, 3.800, -4.360, 2.450, -3.180, 1.030, …
$ `1963` <dbl> -0.429, 3.410, 23.400, 3.800, 3.380, 2.450, -3.930, -3.740, 4…
$ `1964` <dbl> -0.374, 3.420, 2.160, 3.800, 9.360, 2.450, 8.760, 11.400, 4.7…
$ `1965` <dbl> -0.124, 3.650, 3.530, 3.800, 5.710, 2.450, 7.640, 4.390, 3.08…
$ `1966` <dbl> -1.370, 3.750, -7.730, 3.800, 4.040, 2.450, -0.829, 3.670, 0.…
$ `1967` <dbl> 0.310, 3.760, 5.740, 3.800, 4.040, 2.450, 1.220, 3.310, 4.800…
$ `1968` <dbl> 1.06, 3.61, 8.40, 3.80, -3.19, 2.45, 2.87, 4.81, 3.87, 3.90, …
$ `1969` <dbl> -0.883, 3.400, 6.480, 3.800, 1.150, 2.450, 7.080, 0.450, 3.70…
$ `1970` <dbl> -0.514, 3.690, 6.830, 3.800, 4.510, 2.450, 3.850, 6.690, 4.00…
$ `1971` <dbl> -7.160, 4.000, -11.100, -0.603, 3.880, 4.800, 2.130, 1.650, 2…
$ `1972` <dbl> -4.360, 3.920, 17.500, 2.760, -1.970, 4.720, 0.370, -0.430, 0…
$ `1973` <dbl> 8.580, 4.980, 0.285, 2.630, 5.680, 6.090, 2.020, 7.380, 3.820…
$ `1974` <dbl> 2.750, 0.373, 3.010, 0.870, 0.714, 1.700, 3.700, 1.070, 0.831…
$ `1975` <dbl> 2.5300, 0.3480, 3.8900, -3.5900, -7.3700, -6.2900, -2.2400, -…
$ `1976` <dbl> 2.330, 0.400, 3.420, -0.531, -7.630, -8.910, -1.580, 2.850, 2…
$ `1977` <dbl> -9.2400, 0.4380, 5.7700, -0.6340, -1.8700, 8.2100, 4.8600, 0.…
$ `1978` <dbl> 5.210, 0.460, 9.420, -1.920, -7.870, 4.990, -4.730, 0.760, 1.…
$ `1979` <dbl> -2.180, 0.487, 5.750, -3.670, -2.600, 12.600, 5.440, -2.140, …
$ `1980` <dbl> 0.1680, 0.7060, -1.2600, -2.1000, -0.4710, 8.3500, 0.0163, -1…
$ `1981` <dbl> 10.700, 0.536, -0.656, -4.970, -7.610, 6.330, -6.960, -0.756,…
$ `1982` <dbl> 9.0500, 0.5500, 3.0800, -4.0500, -3.5200, 1.5500, -4.5000, 0.…
$ `1983` <dbl> 3.59000, 0.58400, 1.89000, -3.47000, 0.52000, 8.03000, 2.6700…
$ `1984` <dbl> -1.830, 0.569, 2.270, -2.810, 2.440, 8.850, 0.681, -0.516, 5.…
$ `1985` <dbl> -3.280, 0.523, 2.020, -1.370, 0.316, 9.540, -8.340, -0.847, 3…
$ `1986` <dbl> 7.170, 0.635, -3.790, 0.612, 0.332, 10.700, 5.880, 2.330, 0.6…
$ `1987` <dbl> -17.3000, 0.6290, -3.3100, 3.7300, 4.3100, 11.3000, 1.2500, -…
$ `1988` <dbl> -9.660, 0.633, -4.670, 3.540, 3.020, 10.000, -3.220, 0.453, 2…
$ `1989` <dbl> -2.410, 0.754, 0.771, 2.780, -2.140, 7.540, -8.340, 0.135, 2.…
$ `1990` <dbl> -5.5800, 0.8930, -3.9100, 0.8110, -3.1700, 3.2300, -3.2200, -…
$ `1991` <dbl> -0.572, -28.900, -3.490, -1.470, -2.030, 1.540, 9.290, -13.30…
$ `1992` <dbl> -7.950, -8.100, -0.752, -3.740, -8.830, -0.632, 8.540, -40.80…
$ `1993` <dbl> -13.900, 8.780, -4.440, -5.650, -26.400, 3.080, 4.660, -4.660…
$ `1994` <dbl> -10.400, 7.440, -3.070, -1.650, -1.860, 3.660, 4.770, 8.960, …
$ `1995` <dbl> 20.300, 12.600, 1.710, -0.114, 11.600, -6.630, -3.910, 8.900,…
$ `1996` <dbl> 2.660, 8.650, 1.940, 3.090, 16.600, 3.900, 4.460, 6.080, 2.48…
$ `1997` <dbl> 2.8200, -10.6000, -0.5580, 8.5900, 2.7000, 2.3400, 7.0500, 3.…
$ `1998` <dbl> 2.8300, 12.3000, 3.5000, 3.3000, -2.6300, 1.8200, 2.7900, 7.6…
$ `1999` <dbl> 2.7100, 9.5800, 1.6800, 4.0100, 0.3870, 1.6900, -4.4500, 3.35…
$ `2000` <dbl> -1.0500, 6.7900, 0.9950, 0.4010, -0.0561, -0.6830, -1.8500, 6…
$ `2001` <dbl> -10.400, 6.690, 1.130, 10.000, -0.171, 0.251, -5.470, 10.100,…
$ `2002` <dbl> 22.1000, 2.8600, 2.5500, 3.5800, 10.7000, 0.8110, -12.0000, 1…
$ `2003` <dbl> 8.040, 5.450, 5.460, 4.170, -0.247, 3.670, 7.770, 14.400, 3.0…
$ `2004` <dbl> 2.5000, 5.3600, 3.8400, 4.1800, 7.4500, 5.7500, 7.9200, 10.90…
$ `2005` <dbl> 8.6100, 4.9600, 3.8000, 4.2100, 16.6000, 3.2900, 8.1200, 14.3…
$ `2006` <dbl> 1.590, 5.270, 0.188, 2.370, 15.000, 11.300, 7.250, 13.100, 1.…
$ `2007` <dbl> 10.800, 5.410, 1.850, -1.700, 19.600, 5.780, 7.440, 13.600, 2…
$ `2008` <dbl> 0.117, 6.840, 0.472, -5.600, 10.600, 0.378, 5.570, 6.690, 0.7…
$ `2009` <dbl> 17.300, 2.910, 0.179, -6.310, -0.464, -11.700, -0.276, -15.00…
$ `2010` <dbl> 5.1700, 2.9800, 2.0600, -4.7800, 0.5940, -8.5300, 7.9400, 1.1…
$ `2011` <dbl> 3.8500, 2.4900, 0.8570, -4.3000, 1.0300, -2.9600, 7.6500, 3.6…
$ `2012` <dbl> 11.200, 2.280, 1.160, NA, 2.130, 2.790, 0.761, 6.920, 1.780, …
$ `2013` <dbl> 1.130, 1.720, 1.610, NA, 1.030, 0.468, 3.090, 2.980, 1.170, 0…
$ `2014` <dbl> 0.837, 2.610, 2.180, NA, 2.240, 1.620, -0.622, 4.050, 1.410, …
$ `2015` <dbl> 2.110, 3.820, 2.100, NA, 2.460, 1.900, -0.128, 4.290, 1.480, …
$ `2016` <dbl> 2.680, 4.720, 2.360, NA, 2.770, 2.200, 0.367, 4.490, 1.730, 1…
$ `2017` <dbl> 2.760, 5.030, 2.500, NA, 0.262, 2.200, 0.861, 4.790, 1.700, 1…
$ `2018` <dbl> 3.020, 5.030, 2.630, NA, 3.460, 2.200, 0.861, 4.790, 1.710, 1…
$ `2019` <dbl> 3.380, 5.230, 2.680, NA, 3.550, 2.200, 0.861, 4.790, 1.770, 0…Again, we will use the pivot_longer() to transform the data to long format. We will also again change the country variable to be Country by using the rename() function , and we will make the Year varaible numeric using the mutate() function.
We will use the drop_na() function of the tidyr package to drop all years with missing data.
gdp_growth %<>%
pivot_longer(cols = -country,
names_to = "Year",
values_to = "gdp_growth") %>%
rename(Country=country) %>%
mutate(Year =as.numeric(Year)) %>%
# tidyr::drop_na() %>% # not sure we need this..
rename(`GDP Growth/Capita (%)` = gdp_growth)Now let’s see how this data has changed:
# A tibble: 6 x 3
Country Year `GDP Growth/Capita (%)`
<chr> <dbl> <dbl>
1 Afghanistan 1801 NA
2 Afghanistan 1802 NA
3 Afghanistan 1803 NA
4 Afghanistan 1804 NA
5 Afghanistan 1805 NA
6 Afghanistan 1806 NA# A tibble: 219 x 2
Year n
<dbl> <int>
1 1801 194
2 1802 194
3 1803 194
4 1804 194
5 1805 194
6 1806 194
7 1807 194
8 1808 194
9 1809 194
10 1810 194
# … with 209 more rowsEnergy Use per Person
# A tibble: 6 x 57
country `1960` `1961` `1962` `1963` `1964` `1965` `1966` `1967` `1968` `1969`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Albania NA NA NA NA NA NA NA NA NA NA
2 Algeria NA NA NA NA NA NA NA NA NA NA
3 Angola NA NA NA NA NA NA NA NA NA NA
4 Antigu… NA NA NA NA NA NA NA NA NA NA
5 Argent… NA NA NA NA NA NA NA NA NA NA
6 Armenia NA NA NA NA NA NA NA NA NA NA
# … with 46 more variables: `1970` <dbl>, `1971` <dbl>, `1972` <dbl>,
# `1973` <dbl>, `1974` <dbl>, `1975` <dbl>, `1976` <dbl>, `1977` <dbl>,
# `1978` <dbl>, `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>,
# `1983` <dbl>, `1984` <dbl>, `1985` <dbl>, `1986` <dbl>, `1987` <dbl>,
# `1988` <dbl>, `1989` <dbl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>,
# `1993` <dbl>, `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>,
# `1998` <dbl>, `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>,
# `2003` <dbl>, `2004` <dbl>, `2005` <dbl>, `2006` <dbl>, `2007` <dbl>,
# `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>,
# `2013` <dbl>, `2014` <dbl>, `2015` <dbl>Rows: 169
Columns: 57
$ country <chr> "Albania", "Algeria", "Angola", "Antigua and Barbuda", "Argen…
$ `1960` <dbl> NA, NA, NA, NA, NA, NA, 3060, 1550, NA, NA, NA, NA, NA, NA, 2…
$ `1961` <dbl> NA, NA, NA, NA, NA, NA, 3120, 1550, NA, NA, NA, NA, NA, NA, 2…
$ `1962` <dbl> NA, NA, NA, NA, NA, NA, 3170, 1680, NA, NA, NA, NA, NA, NA, 2…
$ `1963` <dbl> NA, NA, NA, NA, NA, NA, 3280, 1820, NA, NA, NA, NA, NA, NA, 3…
$ `1964` <dbl> NA, NA, NA, NA, NA, NA, 3350, 1860, NA, NA, NA, NA, NA, NA, 3…
$ `1965` <dbl> NA, NA, NA, NA, NA, NA, 3460, 1850, NA, NA, NA, NA, NA, NA, 3…
$ `1966` <dbl> NA, NA, NA, NA, NA, NA, 3550, 1900, NA, NA, NA, NA, NA, NA, 3…
$ `1967` <dbl> NA, NA, NA, NA, NA, NA, 3690, 1920, NA, NA, NA, NA, NA, NA, 3…
$ `1968` <dbl> NA, NA, NA, NA, NA, NA, 3760, 2050, NA, NA, NA, NA, NA, NA, 3…
$ `1969` <dbl> NA, NA, NA, NA, NA, NA, 3790, 2180, NA, NA, NA, NA, NA, NA, 3…
$ `1970` <dbl> NA, NA, NA, NA, NA, NA, 4060, 2420, NA, NA, NA, NA, NA, NA, 4…
$ `1971` <dbl> 785.0, 232.0, 556.0, NA, 1380.0, NA, 3990.0, 2510.0, NA, NA, …
$ `1972` <dbl> 866.0, 261.0, 584.0, NA, 1380.0, NA, 4040.0, 2630.0, NA, NA, …
$ `1973` <dbl> 763.0, 305.0, 568.0, NA, 1410.0, NA, 4260.0, 2830.0, NA, NA, …
$ `1974` <dbl> 777.0, 319.0, 565.0, NA, 1420.0, NA, 4290.0, 2730.0, NA, NA, …
$ `1975` <dbl> 827.0, 330.0, 536.0, NA, 1380.0, NA, 4350.0, 2650.0, NA, NA, …
$ `1976` <dbl> 891, 367, 515, NA, 1400, NA, 4410, 2870, NA, NA, 9580, 98, NA…
$ `1977` <dbl> 924.0, 399.0, 494.0, NA, 1420.0, NA, 4670.0, 2800.0, NA, NA, …
$ `1978` <dbl> 1010.0, 477.0, 527.0, NA, 1430.0, NA, 4630.0, 2890.0, NA, NA,…
$ `1979` <dbl> 864.0, 586.0, 518.0, NA, 1480.0, NA, 4680.0, 3140.0, NA, NA, …
$ `1980` <dbl> 1150, 579, 511, NA, 1490, NA, 4740, 3070, NA, NA, 7790, 103, …
$ `1981` <dbl> 989, 611, 497, NA, 1430, NA, 4690, 2900, NA, NA, 8300, 102, N…
$ `1982` <dbl> 967, 771, 473, NA, 1420, NA, 4820, 2830, NA, NA, 9070, 105, N…
$ `1983` <dbl> 1000, 808, 469, NA, 1420, NA, 4560, 2840, NA, NA, 8500, 105, …
$ `1984` <dbl> 1020, 776, 458, NA, 1450, NA, 4650, 2950, NA, NA, 8830, 104, …
$ `1985` <dbl> 917, 786, 470, NA, 1360, NA, 4600, 3050, NA, NA, 9920, 107, N…
$ `1986` <dbl> 964, 862, 462, NA, 1420, NA, 4620, 3060, NA, NA, 10300, 111, …
$ `1987` <dbl> 922, 828, 461, NA, 1480, NA, 4770, 3170, NA, NA, 9520, 107, N…
$ `1988` <dbl> 928, 850, 467, NA, 1500, NA, 4700, 3200, NA, NA, 10500, 114, …
$ `1989` <dbl> 896, 820, 465, NA, 1440, NA, 5000, 3140, NA, NA, 10200, 117, …
$ `1990` <dbl> 813, 856, 483, 1480, 1410, 2180, 5060, 3240, 3170, 2520, 1060…
$ `1991` <dbl> 573, 884, 480, NA, 1430, 2320, 4930, 3420, 3090, NA, 10100, 1…
$ `1992` <dbl> 418, 884, 467, NA, 1480, 1200, 4960, 3250, 2460, NA, 10800, 1…
$ `1993` <dbl> 412, 868, 468, NA, 1470, 652, 5150, 3260, 2180, NA, 11100, 12…
$ `1994` <dbl> 441, 819, 459, NA, 1540, 420, 5090, 3230, 1950, NA, 11600, 12…
$ `1995` <dbl> 417, 839, 445, NA, 1540, 511, 5130, 3370, 1810, NA, 11400, 13…
$ `1996` <dbl> 448, 798, 445, NA, 1580, 562, 5390, 3580, 1510, NA, 11100, 13…
$ `1997` <dbl> 385, 805, 443, NA, 1610, 594, 5470, 3550, 1440, NA, 12200, 13…
$ `1998` <dbl> 427, 821, 430, NA, 1650, 610, 5550, 3610, 1490, NA, 12400, 13…
$ `1999` <dbl> 576, 864, 439, NA, 1660, 594, 5610, 3590, 1370, NA, 11900, 13…
$ `2000` <dbl> 580, 866, 437, NA, 1660, 656, 5640, 3570, 1400, NA, 12000, 13…
$ `2001` <dbl> 597, 856, 442, NA, 1560, 657, 5450, 3760, 1410, NA, 11700, 14…
$ `2002` <dbl> 660, 904, 447, NA, 1500, 618, 5570, 3770, 1410, NA, 11500, 15…
$ `2003` <dbl> 648, 949, 466, NA, 1590, 657, 5570, 3970, 1480, NA, 11600, 15…
$ `2004` <dbl> 715, 948, 462, 1530, 1720, 698, 5600, 4010, 1540, 2060, 10900…
$ `2005` <dbl> 720, 974, 431, 1530, 1710, 843, 5560, 4090, 1600, 2110, 11700…
$ `2006` <dbl> 707, 1030, 456, 1580, 1840, 865, 5710, 4080, 1560, 2100, 1160…
$ `2007` <dbl> 680, 1070, 470, 1600, 1850, 973, 5870, 4020, 1410, 2070, 1120…
$ `2008` <dbl> 711, 1070, 491, NA, 1920, 1030, 5960, 4030, 1520, NA, 11300, …
$ `2009` <dbl> 732, 1150, 514, NA, 1850, 904, 5860, 3800, 1330, NA, 10300, 1…
$ `2010` <dbl> 729, 1110, 521, NA, 1910, 863, 5790, 4050, 1280, NA, 10200, 2…
$ `2011` <dbl> 765, 1140, 522, NA, 1930, 944, 5750, 3920, 1370, NA, 9910, 20…
$ `2012` <dbl> 688, 1220, 553, NA, 1920, 1030, 5570, 3890, 1470, NA, 9660, 2…
$ `2013` <dbl> 801, 1240, 534, NA, 1950, 1000, 5460, 3920, 1470, NA, 10400, …
$ `2014` <dbl> 808, 1320, 545, NA, 2020, 1020, 5330, 3760, 1500, NA, 10600, …
$ `2015` <dbl> NA, NA, NA, NA, NA, NA, 5480, 3800, NA, NA, NA, NA, NA, NA, 4…energy_use %<>%
pivot_longer(cols = -country, names_to = "Year", values_to = "energy_use") %>%
rename(Country=country) %>%
mutate(Year = as.numeric(Year)) %>%
rename(`Energy Use (kg, oil-eq./capita)` = energy_use)Rows: 9,464
Columns: 3
$ Country <chr> "Albania", "Albania", "Albania", "A…
$ Year <dbl> 1960, 1961, 1962, 1963, 1964, 1965,…
$ `Energy Use (kg, oil-eq./capita)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…Crude Mortality Rate
# A tibble: 6 x 64
`Data Source` `World Developm… ...3 ...4 ...5 ...6 ...7 ...8 ...9 ...10
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Last Updated… 43819 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
3 Country Name Country Code Indi… Indi… 1960 1961 1962 1963 1964 1965
4 Aruba ABW Deat… SP.D… 6.38… 6.24… 6.11… 6.01… 5.91… 5.83…
5 Afghanistan AFG Deat… SP.D… 32.2… 31.6… 31.0… 30.5… 30.0… 29.5…
6 Angola AGO Deat… SP.D… 27.0… 26.8… 26.6… 26.4… 26.1… 25.9…
# … with 54 more variables: ...11 <chr>, ...12 <chr>, ...13 <chr>, ...14 <chr>,
# ...15 <chr>, ...16 <chr>, ...17 <chr>, ...18 <chr>, ...19 <chr>,
# ...20 <chr>, ...21 <chr>, ...22 <chr>, ...23 <chr>, ...24 <chr>,
# ...25 <chr>, ...26 <chr>, ...27 <chr>, ...28 <chr>, ...29 <chr>,
# ...30 <chr>, ...31 <chr>, ...32 <chr>, ...33 <chr>, ...34 <chr>,
# ...35 <chr>, ...36 <chr>, ...37 <chr>, ...38 <chr>, ...39 <chr>,
# ...40 <chr>, ...41 <chr>, ...42 <chr>, ...43 <chr>, ...44 <chr>,
# ...45 <chr>, ...46 <chr>, ...47 <chr>, ...48 <chr>, ...49 <chr>,
# ...50 <chr>, ...51 <chr>, ...52 <chr>, ...53 <chr>, ...54 <chr>,
# ...55 <chr>, ...56 <chr>, ...57 <chr>, ...58 <chr>, ...59 <chr>,
# ...60 <chr>, ...61 <chr>, ...62 <chr>, ...63 <chr>, ...64 <chr>We can see that there are a couple of empty rows which indicate when the data was updated. We can also see that the columns really start at the 3rd row. So first we will repace the column names with the 3rd row. Then we will remove the first 3 rows.
[1] "Data Source" "World Development Indicators"
[3] "...3" "...4"
[5] "...5" "...6"
[7] "...7" "...8"
[9] "...9" "...10"
[11] "...11" "...12"
[13] "...13" "...14"
[15] "...15" "...16"
[17] "...17" "...18"
[19] "...19" "...20"
[21] "...21" "...22"
[23] "...23" "...24"
[25] "...25" "...26"
[27] "...27" "...28"
[29] "...29" "...30"
[31] "...31" "...32"
[33] "...33" "...34"
[35] "...35" "...36"
[37] "...37" "...38"
[39] "...39" "...40"
[41] "...41" "...42"
[43] "...43" "...44"
[45] "...45" "...46"
[47] "...47" "...48"
[49] "...49" "...50"
[51] "...51" "...52"
[53] "...53" "...54"
[55] "...55" "...56"
[57] "...57" "...58"
[59] "...59" "...60"
[61] "...61" "...62"
[63] "...63" "...64" [1] "Country Name" "Country Code" "Indicator Name" "Indicator Code"
[5] "1960" "1961" "1962" "1963"
[9] "1964" "1965" "1966" "1967"
[13] "1968" "1969" "1970" "1971"
[17] "1972" "1973" "1974" "1975"
[21] "1976" "1977" "1978" "1979"
[25] "1980" "1981" "1982" "1983"
[29] "1984" "1985" "1986" "1987"
[33] "1988" "1989" "1990" "1991"
[37] "1992" "1993" "1994" "1995"
[41] "1996" "1997" "1998" "1999"
[45] "2000" "2001" "2002" "2003"
[49] "2004" "2005" "2006" "2007"
[53] "2008" "2009" "2010" "2011"
[57] "2012" "2013" "2014" "2015"
[61] "2016" "2017" "2018" "2019" Rows: 264
Columns: 64
$ `Country Name` <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorr…
$ `Country Code` <chr> "ABW", "AFG", "AGO", "ALB", "AND", "ARB", "ARE", "AR…
$ `Indicator Name` <chr> "Death rate, crude (per 1,000 people)", "Death rate,…
$ `Indicator Code` <chr> "SP.DYN.CDRT.IN", "SP.DYN.CDRT.IN", "SP.DYN.CDRT.IN"…
$ `1960` <chr> "6.3879999999999999", "32.219000000000001", "27.0970…
$ `1961` <chr> "6.2409999999999997", "31.649000000000001", "26.8590…
$ `1962` <chr> "6.1180000000000003", "31.093", "26.626999999999999"…
$ `1963` <chr> "6.0119999999999996", "30.550999999999998", "26.407"…
$ `1964` <chr> "5.9199999999999999", "30.021999999999998", "26.1939…
$ `1965` <chr> "5.8390000000000004", "29.501000000000001", "25.9660…
$ `1966` <chr> "5.7699999999999996", "28.984999999999999", "25.6900…
$ `1967` <chr> "5.7160000000000002", "28.468", "25.341999999999999"…
$ `1968` <chr> "5.6820000000000004", "27.946000000000002", "24.916"…
$ `1969` <chr> "5.6660000000000004", "27.417999999999999", "24.4179…
$ `1970` <chr> "5.6710000000000003", "26.879999999999999", "23.872"…
$ `1971` <chr> "5.6980000000000004", "26.334", "23.312000000000001"…
$ `1972` <chr> "5.7460000000000004", "25.780999999999999", "22.7770…
$ `1973` <chr> "5.8120000000000003", "25.222000000000001", "22.2959…
$ `1974` <chr> "5.8929999999999998", "24.658000000000001", "21.8850…
$ `1975` <chr> "5.9809999999999999", "24.087", "21.547999999999998"…
$ `1976` <chr> "6.0700000000000003", "23.507999999999999", "21.276"…
$ `1977` <chr> "6.157", "22.920000000000002", "21.047000000000001",…
$ `1978` <chr> "6.2359999999999998", "22.324000000000002", "20.8389…
$ `1979` <chr> "6.3079999999999998", "21.719999999999999", "20.6469…
$ `1980` <chr> "6.3760000000000003", "21.109000000000002", "20.4669…
$ `1981` <chr> "6.444", "20.489999999999998", "20.297999999999998",…
$ `1982` <chr> "6.5190000000000001", "19.864999999999998", "20.145"…
$ `1983` <chr> "6.6020000000000003", "19.239999999999998", "20.009"…
$ `1984` <chr> "6.6929999999999996", "18.617999999999999", "19.8889…
$ `1985` <chr> "6.7850000000000001", "18.004999999999999", "19.7890…
$ `1986` <chr> "6.8730000000000002", "17.405999999999999", "19.7100…
$ `1987` <chr> "6.9480000000000004", "16.826000000000001", "19.651"…
$ `1988` <chr> "7.0049999999999999", "16.268000000000001", "19.6099…
$ `1989` <chr> "7.0430000000000001", "15.738", "19.579000000000001"…
$ `1990` <chr> "7.0590000000000002", "15.241", "19.555", "5.9850000…
$ `1991` <chr> "7.0540000000000003", "14.782999999999999", "19.5330…
$ `1992` <chr> "7.0339999999999998", "14.362", "19.506", "6.1550000…
$ `1993` <chr> "7.0049999999999999", "13.974", "19.463999999999999"…
$ `1994` <chr> "6.9729999999999999", "13.616", "19.396000000000001"…
$ `1995` <chr> "6.9429999999999996", "13.282", "19.292000000000002"…
$ `1996` <chr> "6.9219999999999997", "12.964", "19.146000000000001"…
$ `1997` <chr> "6.9109999999999996", "12.654999999999999", "18.9520…
$ `1998` <chr> "6.915", "12.348000000000001", "18.706", "6.06700000…
$ `1999` <chr> "6.9340000000000002", "12.037000000000001", "18.404"…
$ `2000` <chr> "6.9710000000000001", "11.718", "18.036000000000001"…
$ `2001` <chr> "7.0220000000000002", "11.387", "17.597000000000001"…
$ `2002` <chr> "7.0839999999999996", "11.048", "17.09", "5.891", NA…
$ `2003` <chr> "7.1539999999999999", "10.704000000000001", "16.5219…
$ `2004` <chr> "7.2329999999999997", "10.356", "15.903", "6.0609999…
$ `2005` <chr> "7.3200000000000003", "10.003", "15.24", "6.20600000…
$ `2006` <chr> "7.4180000000000001", "9.6449999999999996", "14.539"…
$ `2007` <chr> "7.5270000000000001", "9.2870000000000008", "13.815"…
$ `2008` <chr> "7.6479999999999997", "8.9320000000000004", "13.0850…
$ `2009` <chr> "7.7800000000000002", "8.5839999999999996", "12.3670…
$ `2010` <chr> "7.9180000000000001", "8.25", "11.68", "6.8410000000…
$ `2011` <chr> "8.0609999999999999", "7.9359999999999999", "11.039"…
$ `2012` <chr> "8.2050000000000001", "7.6449999999999996", "10.4510…
$ `2013` <chr> "8.3469999999999995", "7.3799999999999999", "9.92099…
$ `2014` <chr> "8.4879999999999995", "7.141", "9.4540000000000006",…
$ `2015` <chr> "8.6270000000000007", "6.9290000000000003", "9.05199…
$ `2016` <chr> "8.7650000000000006", "6.742", "8.7159999999999993",…
$ `2017` <chr> "8.907", "6.5750000000000002", "8.4320000000000004",…
$ `2018` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `2019` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …That is looking better! However, we also want to remove some variables like: Country Code, Indicator Name, and Indicator Code. We can do that using the select() functio of the dplyr package. We can use the minus sign - to indicate what variables we dont want to keep.
mortality %<>%
select(-`Country Code`,
-`Indicator Name`,
-`Indicator Code`) %>%
rename(Country = "Country Name") %>%
#gather(key = Year, value = `Deaths/1000 People`, -Country) %>%
pivot_longer(cols = -Country,
names_to = "Year",
values_to = "Deaths/1000 People") %>%
mutate(Year = as.numeric(Year)) %>%
mutate(`Deaths/1000 People` = as.numeric(`Deaths/1000 People`))# A tibble: 6 x 3
Country Year `Deaths/1000 People`
<chr> <dbl> <dbl>
1 Aruba 1960 6.39
2 Aruba 1961 6.24
3 Aruba 1962 6.12
4 Aruba 1963 6.01
5 Aruba 1964 5.92
6 Aruba 1965 5.84US-specific Data
Disasters
# A tibble: 6 x 57
Year `Drought Count` `Drought Cost` `Drought Lower … `Drought Upper …
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1980 1 33.2 26.4 39.6
2 1981 0 0 0 0
3 1982 0 0 0 0
4 1983 1 7.8 5.5 9
5 1984 0 0 0 0
6 1985 0 0 0 0
# … with 52 more variables: `Drought Lower 90` <dbl>, `Drought Upper 90` <dbl>,
# `Drought Lower 95` <dbl>, `Drought Upper 95` <dbl>, `Flooding Count` <dbl>,
# `Flooding Cost` <dbl>, `Flooding Lower 75` <dbl>, `Flooding Upper
# 75` <dbl>, `Flooding Lower 90` <dbl>, `Flooding Upper 90` <dbl>, `Flooding
# Lower 95` <dbl>, `Flooding Upper 95` <dbl>, `Freeze Count` <dbl>, `Freeze
# Cost` <dbl>, `Freeze Lower 75` <dbl>, `Freeze Upper 75` <dbl>, `Freeze
# Lower 90` <dbl>, `Freeze Upper 90` <dbl>, `Freeze Lower 95` <dbl>, `Freeze
# Upper 95` <dbl>, `Severe Storm Count` <dbl>, `Severe Storm Cost` <dbl>,
# `Severe Storm Lower 75` <dbl>, `Severe Storm Upper 75` <dbl>, `Severe Storm
# Lower 90` <dbl>, `Severe Storm Upper 90` <dbl>, `Severe Storm Lower
# 95` <dbl>, `Severe Storm Upper 95` <dbl>, `Tropical Cyclone Count` <dbl>,
# `Tropical Cyclone Cost` <dbl>, `Tropical Cyclone Lower 75` <dbl>, `Tropical
# Cyclone Upper 75` <dbl>, `Tropical Cyclone Lower 90` <dbl>, `Tropical
# Cyclone Upper 90` <dbl>, `Tropical Cyclone Lower 95` <dbl>, `Tropical
# Cyclone Upper 95` <dbl>, `Wildfire Count` <dbl>, `Wildfire Cost` <dbl>,
# `Wildfire Lower 75` <dbl>, `Wildfire Upper 75` <dbl>, `Wildfire Lower
# 90` <dbl>, `Wildfire Upper 90` <dbl>, `Wildfire Lower 95` <dbl>, `Wildfire
# Upper 95` <dbl>, `Winter Storm Count` <dbl>, `Winter Storm Cost` <dbl>,
# `Winter Storm Lower 75` <dbl>, `Winter Storm Upper 75` <dbl>, `Winter Storm
# Lower 90` <dbl>, `Winter Storm Upper 90` <dbl>, `Winter Storm Lower
# 95` <dbl>, `Winter Storm Upper 95` <dbl>We are specifically interested in the Year and the variables that contain the word "Count" so we will select them using the select() and contains() functions in the dplyr package. Since we are selecting for variables with the word "Count" we need to use quotation marks around it. Selecting for the variable year does not require this as that is actually the name of one of the existing variables.
# A tibble: 6 x 8
Year `Drought Count` `Flooding Count` `Freeze Count` `Severe Storm C…
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1980 1 1 0 0
2 1981 0 0 1 1
3 1982 0 0 0 2
4 1983 1 2 1 0
5 1984 0 0 0 2
6 1985 0 0 1 0
# … with 3 more variables: `Tropical Cyclone Count` <dbl>, `Wildfire
# Count` <dbl>, `Winter Storm Count` <dbl>Now we want to create a new variable that will be the sum of all the different types of disasters for each year.
We can create this ne variable using the mutate() function of dplyr and we will use the base rowSums() function to perform the calculation. We dont want to include the Year variable in our sum, so we can exclude it using the selectfunction within the rowSums() function. However, to do so we need to indicate that we are using the data that we already used as input to our mutate() and rowSums() functions. We can do so by using a ..
Rows: 40
Columns: 9
$ Year <dbl> 1980, 1981, 1982, 1983, 1984, 1985, 1986, 19…
$ `Drought Count` <dbl> 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0,…
$ `Flooding Count` <dbl> 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,…
$ `Freeze Count` <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,…
$ `Severe Storm Count` <dbl> 0, 1, 2, 0, 2, 0, 1, 0, 0, 1, 1, 1, 4, 1, 1,…
$ `Tropical Cyclone Count` <dbl> 1, 0, 0, 1, 0, 3, 0, 0, 0, 1, 0, 1, 2, 0, 1,…
$ `Wildfire Count` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,…
$ `Winter Storm Count` <dbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 2,…
$ Disasters <dbl> 3, 2, 3, 5, 2, 5, 2, 0, 1, 5, 3, 4, 7, 5, 6,…Temperature
us_temperature <- read_csv(here("docs/temperature.txt"),skip=4,na="-99")
sapply(us_temperature, class) Date Value Anomaly
"numeric" "numeric" "numeric" Analysis data frame
[1] "Country" "Year" "CO2 Emissions (Mg)"[1] "Country" "Year" "GDP Growth/Capita (%)"[1] "Country" "Year"
[3] "Energy Use (kg, oil-eq./capita)"[1] "Country" "Year" "Deaths/1000 People"df_wide <- CO2_emissions %>%
full_join(gdp_growth, by=c("Country", "Year")) %>%
full_join(energy_use, by=c("Country", "Year")) %>%
full_join(mortality, by=c("Country", "Year"))
df_long <- df_wide %>%
gather(key=Indicator,value=Value,-Country, -Year) %>%
mutate(Region=case_when(Country=="United States" ~ "United States",
Country!="United States" ~ "Rest of the World"),
Type="Global")
setequal(sapply(df_long, class),sapply(us_disaster, class))[1] TRUE[1] TRUE Country Year Indicator Value Region Type
"character" "character" "character" "numeric" "character" "character" df_long$Country <- as.factor(df_long$Country)
df_long$Year <- as.numeric(df_long$Year)
sapply(df_long,class) Country Year Indicator Value Region Type
"factor" "numeric" "character" "numeric" "character" "character" Data Visualization
CO2 plots
We will rename the Emissions variable so that it will include more information in our plot.
CO2_emissions %>%
# rename(`CO2 Emissions (Mg)`= Emissions) %>%
ggplot(aes(x=Year, y=`CO2 Emissions (Mg)`, group=Country)) +
geom_line(alpha=0.2) +
labs(title = expression("Country CO"[2]*" Emissions per Year , 1751-2014"),
caption = expression("Limited to reporting countries")) +
ylab("Emissions (1M Metric Tonnes)")
CO2_world<-CO2_emissions %>%
# rename(`CO2 Emissions (Mg)`= Emissions) %>%
group_by(Year) %>%
summarise(`CO2 Emissions (Mg)` = sum(`CO2 Emissions (Mg)`, na.rm = TRUE)) %>%
ggplot(aes(x=Year, y=`CO2 Emissions (Mg)`)) +
geom_line() +
labs(title = expression("World CO"[2]*" Emissions per Year , 1751-2014"),
caption = expression("Limited to reporting countries")) +
ylab("Emissions (1M Metric Metric Tonnes)")
CO2_world
Yearly Growth in GDP per Capita
ggplot(gdp_growth, aes(x=Year, y=`GDP Growth/Capita (%)`, group=Country)) +
geom_line(alpha=0.2) +
labs(title = expression("Country GDP Growth per Capita per Year (Annual %), 1801-2019"),
caption = expression("Limited to reporting countries")) +
ylab("GDP Growth per Capita (Annual %)")
gdp_growth %>%
group_by(Year) %>%
summarise(`GDP Growth/Capita (%)` = mean(`GDP Growth/Capita (%)`, na.rm = TRUE)) %>%
ggplot(aes(x=Year, y=`GDP Growth/Capita (%)`)) +
geom_line() +
labs(title = expression("Mean Country GDP Growth per Capita per Year (Annual %), 1801-2019"),
caption = expression("Limited to reporting countries")) +
ylab("GDP Growth per Capita (Annual %)")
Energy Use per Person
ggplot(energy_use, aes(x=Year, y=`Energy Use (kg, oil-eq./capita)`, group=Country)) +
geom_line(alpha=0.2) +
labs(title = expression("Country Energy Use (kg of Oil Equivalent per Capita), 1960-2015",),
caption = expression("Limited to reporting countries")) +
ylab("Energy Use (kg of Oil Equivalent per Capita)")
energy_use %>%
group_by(Year) %>%
summarise(`Energy Use (kg, oil-eq./capita)` = sum(`Energy Use (kg, oil-eq./capita)`, na.rm = TRUE)) %>%
ggplot(aes(x=Year, y=`Energy Use (kg, oil-eq./capita)`)) +
geom_line() +
labs(title = expression("Worldwide Energy Use (kg of Oil Equivalent per Capita), 1960-2015"),
caption = expression("Limited to reporting countries")) +
ylab("Energy Use (kg of Oil Equivalent per Capita)")
Crude Mortality Rate
ggplot(mortality, aes(x=Year, y=`Deaths/1000 People`, group=Country)) +
geom_line(alpha=0.2) +
labs(title = expression("Country Crude Mortality Rate (per 1000 Persons), 1960-2019",),
caption = expression("Limited to reporting countries")) +
ylab("Crude Mortality Rate (per 1000 Persons)")
mortality %>%
group_by(Year) %>%
summarise(`Deaths/1000 People` = mean(`Deaths/1000 People`, na.rm = TRUE)) %>%
ggplot(aes(x=Year, y=`Deaths/1000 People`)) +
geom_line() +
labs(title = expression("Mean Country Crude Mortality Rate (per 1000 Persons), 1960-2019",),
caption = expression("Limited to reporting countries")) +
ylab("Crude Mortality Rate (per 1000 Persons)")
US-specific Data
Subsetting the data
df_long %>%
filter(Type=="Global") %>%
group_by(Year,Indicator) %>%
tally() %>%
ggplot(aes(x=Year, y=n, color=Indicator)) +
geom_vline(xintercept = 1980, linetype=2, color="black") +
geom_vline(xintercept = 2010, linetype=2, color="black") +
geom_line() +
labs(title = "Countries with Complete Data per Year",
subtitle = "Global Data") +
ylab("Countries") +
scale_x_continuous(breaks = seq(1750,2020,by=10),
labels = seq(1750,2020,by=10)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank(),
legend.position = "bottom")
df_long %>%
filter(Region=="United States") %>%
group_by(Indicator) %>%
summarise(Start=min(Year), End=max(Year)) %>%
ggplot(aes(x=Indicator, y=End)) +
geom_hline(yintercept = 1980, linetype=2, color="black") +
geom_hline(yintercept = 2010, linetype=2, color="black") +
geom_segment(aes(x=Indicator,
xend=Indicator,
yend=End,
y=Start)) +
geom_point(aes(x=Indicator, y=Start), shape=16, color="black") +
geom_point(aes(x=Indicator, y=End), shape=21, fill="white", color="black") +
coord_flip() +
labs(title = "Complete Data per Year",
subtitle = "US-specific Data") +
ylab("Countries") +
scale_y_continuous(breaks = seq(1750,2020,by=10),
labels = seq(1750,2020,by=10)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90),
axis.title = element_blank())
animation_1 <- df_long %>%
filter(Type=="Global") %>%
filter(Indicator=="Deaths/1000 People") %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region,alpha=Region)) +
geom_point() +
scale_color_manual(values = c("Red","Black")) +
scale_alpha_manual(values = c(0.1, 1)) +
scale_size_manual(values = c(0.25, 2)) +
labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ylab("Crude Mortality Rate") +
transition_time(as.integer(Year)) +
shadow_wake(wake_length = 1, alpha = FALSE)
animate(animation_1, fps = 10, duration = 5)animation_2 <- df_long %>%
filter(Type=="Global") %>%
filter(Indicator=="Energy Use (kg, oil-eq./capita)") %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
geom_point() +
scale_color_manual(values = c("Red","Black")) +
scale_alpha_manual(values = c(0.1, 1)) +
scale_size_manual(values = c(0.25, 2)) +
labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
ylab("Energy Use per Capita") +
transition_time(as.integer(Year)) +
shadow_wake(wake_length = 1, alpha = FALSE)
animate(animation_2, fps = 10, duration = 5)animation_3 <- df_long %>%
filter(Type=="Global") %>%
filter(Indicator=="GDP Growth/Capita (%)") %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
geom_point() +
scale_color_manual(values = c("Red","Black")) +
scale_alpha_manual(values = c(0.1, 1)) +
scale_size_manual(values = c(0.25, 2)) +
labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
ylab("GDP Growth per Capita (%)") +
transition_time(as.integer(Year)) +
shadow_wake(wake_length = 1, alpha = FALSE)
animate(animation_3, fps = 10, duration = 5)animation_4 <- df_long %>%
filter(Type=="Global") %>%
filter(Indicator=="CO2 Emissions (Mg)") %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
geom_point() +
scale_color_manual(values = c("Red","Black")) +
scale_alpha_manual(values = c(0.1, 1)) +
scale_size_manual(values = c(0.25, 2)) +
labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
ylab("CO2 Emissions (Mg)") +
transition_time(as.integer(Year)) +
shadow_wake(wake_length = 1, alpha = FALSE)
animate(animation_4, fps = 10, duration = 5)Top10<-df_long %>%
filter(Type=="Global") %>%
filter(Indicator=="CO2 Emissions (Mg)") %>%
filter(Year>=1900) %>%
filter(Year<=2010) %>%
group_by(Country) %>%
mutate(max_val = max(Value)) %>%
ungroup() %>%
mutate(rank=dense_rank(-max_val)) %>%
filter(rank<=10) %>%
ggplot(aes(x=Year, y=fct_reorder(Country, Value, max))) +
geom_tile(color="transparent", aes(fill=log(Value))) +
scale_fill_gradientn(colors = c("yellow","red","black")) +
scale_x_continuous(breaks = seq(1900,2010,by=5),
labels = seq(1900,2010,by=5)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title = element_blank(),
legend.position = "bottom") +
labs(title = "Top 10 Emissions-producing Countries in 2010 (1900-2010)",
subtitle = "Ordered by Emissions Produced in 2010",
fill = "Ln(CO2 Emissions (Mg))")
Top10
US-specific
df_long_us <- df_long %>%
filter(Country=="United States")
# Approximated derivative function)
df_long_us <- df_long_us %>%
filter(Year>=1900,
Year<=2010) %>%
group_by(Indicator) %>%
mutate("Change (%)"=((Value/lag(Value))*100)-100,
Mean=mean(Value),
Anomaly=Value-Mean) %>%
ungroup() %>%
mutate(Anomaly_color=ifelse(Anomaly>0,"Positive",
ifelse(Anomaly<0,"Negative","Zero")),
Anomaly_color=factor(Anomaly_color, levels = c("Positive",
"Negative",
"Zero"),
ordered = TRUE))US_Indicators<-df_long_us %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
ggplot(aes(x=Year, y=Value)) +
geom_line() +
facet_wrap(Indicator~., ncol=2, nrow=3, scales = "free_y") +
scale_x_continuous(breaks = seq(1980,2010,by=5),
labels = seq(1980,2010,by=5)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title.y = element_blank()) +
labs(title = "US-specific Indicators (1980-2010)")
US_Indicators
df_long_us %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
ggplot(aes(x=Year, y=`Change (%)`, color=Indicator, fill="transparent")) +
geom_hline(yintercept=0.8, linetype=2) +
geom_hline(yintercept=1.2, linetype=2) +
geom_hline(yintercept = 1, linetype=3) +
geom_line(size=0.5) +
scale_x_continuous(breaks = seq(1980,2010,by=5),
labels = seq(1980,2010,by=5)) +
scale_y_continuous(breaks = seq(-500,1250, by=250),
labels = seq(-500,1250, by=250)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
labs(title = "US-specific Indicators (1980-2010)",
subtitle = "Change (%) Lines")
df_long_us %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
filter(Indicator=="Temperature (Fahrenheit)"|
Indicator=="Energy Use (kg, oil-eq./capita)"|
Indicator=="CO2 Emissions (Mg)") %>%
ggplot(aes(x=Year, y=`Change (%)`, color=Indicator)) +
geom_hline(yintercept=0.8, linetype=2) +
geom_hline(yintercept=1.2, linetype=2) +
geom_hline(yintercept = 1, linetype=3) +
geom_line(size=1) +
scale_x_continuous(breaks = seq(1980,2010,by=5),
labels = seq(1980,2010,by=5)) +
scale_y_continuous(breaks = seq(-10,10, by=1),
labels = seq(-10,10, by=1),
limits = c(-10,10)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
labs(title = "Emissions, Energy Use, and Temperature (1980-2010)",
subtitle = "Change (%) Lines")
df_long_us %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
filter(Indicator=="Temperature (Fahrenheit)"|
Indicator=="Energy Use (kg, oil-eq./capita)"|
Indicator=="CO2 Emissions (Mg)") %>%
ggplot(aes(x=Year, y=`Change (%)`, color=Indicator)) +
geom_hline(yintercept=0.8, linetype=2) +
geom_hline(yintercept=1.2, linetype=2) +
geom_hline(yintercept = 1, linetype=3) +
geom_smooth(size=1, alpha=0.1, aes(fill=Indicator), se=FALSE) +
scale_x_continuous(breaks = seq(1980,2010,by=5),
labels = seq(1980,2010,by=5)) +
scale_y_continuous(breaks = seq(-10,10, by=1),
labels = seq(-10,10, by=1),
limits = c(-10,10)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
labs(title = "US Emissions, Energy Use, and Temperatures (1980-2010)",
subtitle = "Smoothed Change (%) Lines")
df_long_us %>%
filter(Year>=1980) %>%
filter(Year<=2010) %>%
filter(Indicator=="CO2 Emissions (Mg)"|
Indicator=="Temperature (Fahrenheit)") %>%
ggplot(aes(x=Year, y=Value)) +
geom_line() +
scale_x_continuous(breaks = seq(1980,2010,by=5),
labels = seq(1980,2010,by=5)) +
facet_wrap(Indicator~., scales = "free_y", ncol=1) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title = element_blank()) +
labs(title="US Emissions and Temperatures (1980-2010)")
df_long_us %>%
filter(Indicator=="CO2 Emissions (Mg)"|
Indicator=="Temperature (Fahrenheit)") %>%
ggplot(aes(x=Year, y=Value)) +
geom_vline(xintercept = 1980, linetype=2, color="black") +
geom_vline(xintercept = 2010, linetype=2, color="black") +
geom_segment(aes(x=Year, y=Value, xend=Year, yend=Mean,color=Anomaly_color), size=1.25) +
scale_color_manual(values = c("red","blue","gray")) +
geom_hline(aes(yintercept=Mean), linetype=1, color="black") +
scale_x_continuous(breaks = seq(1900,2010,by=5),
labels = seq(1900,2010,by=5)) +
facet_wrap(Indicator~., scales = "free_y", ncol=1) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title = element_blank(),
legend.position = "none") +
labs(title = "US Emissions and Temperatures (1900-2010)",
subtitle = "Indicator Mean Represented by Solid Black Line")
df_long_us %>%
filter(Indicator=="Temperature (Fahrenheit)"|
Indicator=="CO2 Emissions (Mg)") %>%
ggplot(aes(x=Year, y=`Change (%)`)) +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=0, ymax=Inf, alpha=0.25, fill="green") +
annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=0, alpha=0.25, fill="red") +
geom_hline(yintercept=0, linetype=1) +
geom_segment(aes(x=Year, y=`Change (%)`, xend=Year, yend=0), size=1.25) +
facet_wrap(Indicator~., scales = "free_y", ncol=1) +
scale_x_continuous(breaks = seq(1900,2010,by=5),
labels = seq(1900,2010,by=5)) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal") +
labs(title = "US Emissions, Energy Use, and Temperatures (1980-2010)",
subtitle = "Change (%) Lines")
Main plot
library(patchwork)
CO2_world + Top10 + US_Indicators +
plot_layout(widths = c(1, 2), heights = unit(c(2, 5), c('cm', 'null')))
png(here::here("img", "mainplot.png"), width = 900, height = 700)
(CO2_world | Top10)/ US_Indicators+
plot_layout(widths = c(1, 2), heights = unit(c(4, 5), c('cm', 'null')))
dev.off()quartz_off_screen
2 ---
title: "Open Case Studies : CO2 Emissions Over Time"
author: "Michael Ontiveros, Carrie Wright, PhD."
css: style.css
output:
  html_document:
    self_contained: yes
    code_download: yes
    highlight: tango
    number_sections: no
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
---

<style>
#TOC {
  background: url("https://opencasestudies.github.io/img/logo.jpg");
  background-size: contain;
  padding-top: 240px !important;
  background-repeat: no-repeat;
}
</style>

```{r setup, include=FALSE}
knitr::opts_chunk$set(include = TRUE, comment = NA, echo = TRUE,
                      message = FALSE, warning = FALSE, cache = FALSE,
                      fig.align = "center", out.width = '90%')
library(here)
library(knitr)
```

#### {.outline }
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "mainplot.png"))
```

####

## {.disclaimer_block}

**Disclaimer**: The purpose of the [Open Case Studies](https://opencasestudies.github.io){target="_blank"} project is **to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data**. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. 

## **Motivation**
*** 

This case study explores how different countries have contributed to Carbon Dioxide (CO2) emissions over time and how CO2 emission rates may relate to increasing global temperatures and increased rates of natural disasters and storms. This report provides a basis for the motivation: https://www.epa.gov/report-environment/greenhouse-gases.


CO2 makes up the largest proportion of greenhouse gas emissions in the United States:


```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "emissions.jpg"))
```

A variety of sources and sectors contribute to greenhouse gas emissions, with transportation contributing the most metric tons of CO2:


```{r, echo = FALSE, out.width="600px"}
knitr::include_graphics(here::here("img", "sector.png"))
```

So why should we pay attention to greenhouse gases?

According to the [US Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks 2020 Report](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks): 

> Greenhouse gases absorb infrared radiation, thereby trapping heat in the atmosphere and making the planet warmer. The most important greenhouse gases directly emitted by humans include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and several fluorine-containing halogenated substances. Although CO2, CH4, and N2O occur naturally in the atmosphere, human activities have changed their atmospheric concentrations. From the pre- industrial era (i.e., ending about 1750) to 2018, concentrations of these greenhouse gases have increased globally by 46, 165, and 23 percent, respectively (IPCC 2013; NOAA/ESRL 2019a, 2019b, 2019c). 

* IPCC stands for the Intergovernmental Panel on Climate Change

There are many signs that our planet is experiencing warmer temperatures:

```{r, echo = FALSE, out.width="600px"}
knitr::include_graphics(here::here("img", "warming.png"))
```

The connection between greenhouse gas levels and global temperatures and the influence of increased global temperatures on human health are motivated by these reports:

#### {.reference_block}

Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program.  

2020. “Inventory of US Greenhouse Gas Emissions and Sinks: 1990--2018.” EPA 430-R-20-002, Tech. Rep. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.


####

The National Climate Assessment Report states that:

> Heat-trapping gases already in the atmosphere have committed us to a hotter future with more climate-related impacts over the next few decades. The magnitude of climate change beyond the next few decades depends primarily on the amount of heat-trapping gases that human activities emit globally, now and in the future.

See [here](https://www.epa.gov/report-environment/greenhouse-gases) and [here](https://world101.cfr.org/global-era-issues/climate-change/climate-change-adaptations) for more information.

## **Main Questions**
*** 

#### {.main_question_block}
<b><u> Our main question: </u></b>

1) How have global CO2 emission rates changed over time? In particular for the US, and how does the US compare to other countries? 
2) Are US CO2 emissions, global temperatures, and US storm rates associated? 

####

## **Learning Objectives** 
*** 

In this case study, we will explore CO2 emission data from around the world. We will also focus on the US specifically to evaluate patterns of temperatures and storm activity. This case study will particularly focus on visualizations of patterns over time. We will especially focus on using packages and functions from the [`Tidyverse`](https://www.tidyverse.org/){target="_blank"}, such as `plotly`and `gganimate`. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.


*** 


We will begin by loading the packages that we will need:

```{r}
library(here)
library(readxl)
library(readr)
library(dplyr)
library(magrittr)
library(tidyverse)
library(plotly)
library(gganimate)
library(RColorBrewer)
```


 Package   | Use                                                                         
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data
[readxl](https://readxl.tidyverse.org/){target="_blank"}  | to import the excel file data
[readr](https://readr.tidyverse.org/){target="_blank"}  | to import the csv file data
[dplyr](https://dplyr.tidyverse.org/){target="_blank"}  |  to view and wrangle the data
[magrittr](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html){target="_blank"}  |  to use and reassign data objects using the `%<>%`pipe operator
[tidyverse](https://www.tidyverse.org/packages/){target="_blank"}  | to wrangle the data and create ggplot2 plots
[plotyly](https://plotly.com/r/){target="_blank"}  | to make the visualizations
[gganimate](https://gganimate.com/){target="_blank"}  | to make the plots interactive
[RColorBrewer](https://cran.r-project.org/web/packages/RColorBrewer/index.html){target="_blank"}  | to have greater control over the color in our plots

The first time we use a function, we will use the `::` to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.


## **Context**
*** 

Greenhouse gas emissions are due to both natural processes and anthropogenic (human-derived) activities. 

These emissions are one of the contributing factors to rising global temperatures, which can have a great influence on [public health](https://www.epa.gov/climate-indicators/understanding-connections-between-climate-change-and-human-health){target="_blank"}  as illustrated in the following image:

```{r, echo = FALSE, out.width="800px"}
knitr::include_graphics(here::here("img", "health.png"))
```

> Gases in the atmosphere can contribute to climate change both directly and indirectly. Direct effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the substance produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or when a gas affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or albedo). The IPCC developed the Global Warming Potential (GWP) concept to compare the ability of a greenhouse gas to trap heat in the atmosphere relative to another gas.
The GWP of a greenhouse gas is defined as the ratio of the accumulated radiative forcing within a specific time horizon caused by emitting 1 kilogram of the gas, relative to that of the reference gas CO2 (IPCC 2013). Therefore GWP-weighted emissions are provided in million metric tons of CO2 equivalent (MMT CO2 Eq.)


 CO2 is actually the least capable of the greenhouse gases for trapping heat:

```{r, echo = FALSE, out.width="800px"}
knitr::include_graphics(here::here("img", "GWP.png"))
```

However, because CO2 is so much more abundant and stays in the atmosphere so much longer than other greenhouse gases, it has been the largest contributor to global warming.

See [here](https://www.ucsusa.org/resources/why-does-co2-get-more-attention-other-gases#:~:text=CO2%20sticks%20around,oxide%20(N2O){target="_blank"}.)
for more details.


Furthermore, sizing CO2 levels also influence ocean acidity:

```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "oceans.png"))
```

This makes it difficult for organisms to maintain their shells or skeletons that are made of calcium carbonate, thus making it more difficult for these organisms to survive and impacting their role in the ecosystem and food chain. 


Furthermore, greenhouse gas emissions are believed to influence storm rates. 

Indeed events with high levels of precipitation which can induce flooding and property damage are generally increasing around the country:

```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "storms.png"))
```


## **Limitations**
*** 

There are some important considerations regarding this data analysis to keep in mind: 

1) Correlation or association does not imply causation 

2) Limitaiton 2  






## **What are the data?**
*** 

In this case study we will be using data related to CO2 emissions, as well as other data that may influence, be influenced or relate to CO2 emissions. Most of our data was obtained from [Gapminder](https://www.gapminder.org/data/){target="_blank"}, which is a unique nonprofit that provides a variety of data for free.

In their words, Gapminder is...

> Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand.  Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found [here](https://www.gapminder.org/about-gapminder/constitution/).

The data that we will be using from Gapminder was obtained from the [World Bank](https://www.worldbank.org/en/what-we-do){target="_blank"}.


In addition we will use some data that is specific to the United States from the [National Oceanic and Atmospheric Administration (NOAA)] (https://www.noaa.gov/), which is an agency that collects weather and climate data.


Data   | Source         | Orginal Source   | Description | Citation                                                                    
---------- |-------------|-------------|-------------
**CO2 emissions**  | [Gapminder](https://www.gapminder.org/data/){target="_blank"}  | [Carbon Dioxid Information Analysis Center (CDIAC)](https://cdiac.ess-dive.lbl.gov/){target="_blank"}  |  CO2 emissions in tonnes or metric tons (equivalent to approximately 2,204.6 pounds) per person | NA
**GDP per capita, yearly growth**  | [Gapminder](https://www.gapminder.org/data/){target="_blank"}  | [World Bank](https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG){target="_blank"}  |  [Growth Domestic Product](https://www.investopedia.com/terms/g/gdp.asp#:~:text=Gross%20Domestic%20Product%20(GDP)%20is%20the%20monetary%20value%20of%20all,expenditures%2C%20production%2C%20or%20incomes.){target="_blank"}  (which is an overall measure of the health of nation's economy) per person | NA
**Energy use per person**  | [Gapminder](https://www.gapminder.org/data/){target="_blank"}  | [World Bank](https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE){target="_blank"}  |  Use of primary energy before transformation to other end-use fules, by country starting in 1960 | NA
**Crude Mortality Rate**  | [World Bank](https://data.worldbank.org/indicator/SP.DYN.CDRT.IN){target="_blank"}  | [World Bank](https://data.worldbank.org/indicator/SP.DYN.CDRT.IN){target="_blank"} |  Death rate per 1,000 people from 1960 to 2018 by country| NA 
**US Natural Disasters**  | [The National Oceanic and Atmospheric Administration (NOAA)](https://www.ncdc.noaa.gov/billions/time-series){target="_blank"}| [The National Oceanic and Atmospheric Administration (NOAA) ](https://www.ncdc.noaa.gov/billions/time-series){target="_blank"}|  Data from 1980 to 2019 including: <br> -- Drought Count <br> -- Flooding Count <br> -- Freeze Count <br> -- Severe Storm Count <br> -- Tropical Cyclone Count <br> -- Wildfire Count <br> -- Winter Storm Count | NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2020). https://www.ncdc.noaa.gov/billions/, DOI: 10.25921/stkw-7w73
**Temperature**  | [The National Oceanic and Atmospheric Administration (NOAA)](https://www.ncdc.noaa.gov/cag/global/time-series/globe/land_ocean/ytd/5/1880-2020){target="_blank"}  | [The National Oceanic and Atmospheric Administration (NOAA)](https://www.ncdc.noaa.gov/cag/global/time-series/globe/land_ocean/ytd/5/1880-2020){target="_blank"} | National yearly average temperature (in Fahrenheit) from 1895 to 2019 | NOAA National Centers for Environmental Information, State of the Climate: Global Climate Report for March 2020, published online April 2020, retrieved on June 25, 2020 from https://www.ncdc.noaa.gov/sotc/global/202003/supplemental/page-1.


To obtain the temperature data, annual average temperatures were selected as shown in this image:
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "temp.png"))
```

https://www.ncdc.noaa.gov/cag/national/time-series/

## **Data Import**
*** 

To read in the files that were downloaded from the various sources as indicated in the table above, we will use the `read_xlsx()` and `read_xls()` functions of the `readxl` package to import the data from the .xlsx and .xls files respectively and we will use the `read_csv` function of the `readr` package to import the data from the csv files.

```{r}
# xlsx files:
CO2_emissions <- readxl::read_xlsx(here("docs/yearly_co2_emissions_1000_tonnes.xlsx"))
gdp_growth <- readxl::read_xlsx(here("docs/gdp_per_capita_yearly_growth.xlsx"))
energy_use <- readxl::read_xlsx(here("docs/energy_use_per_person.xlsx"))

# xls file:
mortality <- readxl::read_xls(here("docs/API_SP.DYN.CDRT.IN_DS2_en_excel_v2_804384.xls"))
```

For our csv data files, there are some lines that we would like to not import - infact, we will get an error if we try to import them because our table structure will be as r expects. We can do so using the `skip =` argument of the `read_csv()` function. 

Here you can see that the first two rows of the data about US Disasters doesn't have the same number of columns as the subsequent rows. So we want to skip these first two lines, we will use `skip = 2` for this.

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "Disasters.png"))
```
Now looking at the temperature data, we can see that  the first four lines do not have the same number of columns as the subsequent lines. We will skip importing all 4 lines by using `skip = 4`. We can also specify that `NA` values are encoded as `"-99"`. This will replace all instances of `"-99"` with `NA`. We can do this using the `na = ` argument of the `read_csv()` function. We will do so as: `na = "-99"`. The "-99" needs to be in quotation markes becuase this argument expects characters.

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "tempdata.png"))
```

```{r}
#csv files:
us_disaster <- readr::read_csv(here("docs/time-series-US.csv"), skip = 2)
us_temperature <- readr::read_csv(here("docs/temperature.txt"), skip = 4, na ="-99")
```

Great! now we have imported all of the data that we will need.


## **Data Exploration and Wrangling**
*** 

Now we will take a look at our data and wrangle it until it is easy to use to allow us to evaluate how CO2 emissions have changed over time and how emissions may relate to energy use, mortality, GDP etc. 

### Yearly CO~2~ Emissions

First let's take a look at the CO2 data. We can use the base `head()` function to see just the first 6 rows of our data.

We will use the `%>%` pipe which can be used to define the input for later sequential steps. This will make more sense when we have multiple sequential steps using the same data object. To use the pipe notation we need to install and load  the `dplyr` package.

```{r}
CO2_emissions %>%
  head()
```

OK, we can see that our country data makes of the rows and the yearly data makes up the columns. We also see that we have alot of `NA` values.


We can also use the `glimpse()` function of the `dplyr` packge to view our data. This allows us to see more of our data at once. We will see a tiny bit of each variable/column. To do so our data will be displayed with the column names listed on the right.

#### {.scrollable }
```{r}
# Scroll through the output!
CO2_emissions %>%
dplyr::glimpse()
```
####

We can see that we have a large [tibble](https://tibble.tidyverse.org/). A tibble is the tidyverse version of a data frame. It is essentially a table with variable information arranged as columns, and individual observations arranged as rows. We can see that the tibble gives us information about the class of each variable.  For example the `country` variable is made up of character (abbreviated as chr) values. We see that we have 265 different country variables and CO2 emission values for 192 different years (from 1751 to 2014). Recall that the values are emissions in metric tons also called tonnes. We can see that there are fewer `NA` values for later years.

Now we will modify this data to make it more usable for making visualizations. One thing we will use is the `%<>%` opperator which is from the `magrittr` package. This allows us to use our `CO2_emissions` data and reassign it to a modified version at the same time. 

We will use the `pivot_longer()` function of the `dplyr` package to convert our data into what is called long format. This means that we will have more rows and fewer columns than our current format. This is done by collapsing multiple variables into fewer variables.

We want to collapse all of the values for the emission data across the different individual year variables into one new emission variable and we will identify what year they are from using a new `Year` variable.

```{r}
CO2_emissions  %<>%
  pivot_longer(cols = -country, names_to = "Year", values_to = "Emissions")

head(CO2_emissions)
```

We also want to rename the `country` variable to be capitalized. W We can use the `rename()` function of the `dplyr` package to rename this variable. When renaming variables the new name is listed first before the `=`. We will also modify the `Emissions` data by dividing it by 1000 to make the numbers smaller. To do this we will use the `mutate()` function, which is also part of the `dplyr()` package. This function allows us to create and modify variables. You may also note that the `Year` variable is currently of class type character. We would like to change it to be numeric. This can also be accomplished using the `mutate()` function.

```{r}
  
 CO2_emissions  %<>% 
   dplyr::rename(Country=country) %>%
   dplyr::mutate(Emissions = Emissions/1000, 
          Year = as.numeric(Year)) %>%
     rename(`CO2 Emissions (Mg)`= Emissions)

```

Now let's take a look to see how our data has changed:

```{r}

CO2_emissions %>%
head()

```
Great, we can see that now the `Year` variable is of class double (abbreviated `dbl`), which is a numeric class.

### Yearly Growth in GDP per Capita

```{r}
gdp_growth %>%
  head()
```

```{r}
names(gdp_growth)
```

#### {.scrollable}
```{r}
# Scroll through the output!
gdp_growth %>%
glimpse()
```
####

Again, we will use the `pivot_longer()` to transform the data to long format. We will also again change the `country` variable to be `Country` by using the `rename()` function , and we will make the `Year` varaible numeric using the `mutate()` function. 

We will use the `drop_na()` function of the `tidyr` package to drop all years with missing data.

```{r}
# gdp_growth %<>%
#    tidyr::drop_na()
# 
# head(gdp_growth)
```

```{r}
gdp_growth %<>%
  pivot_longer(cols = -country, 
               names_to = "Year", 
               values_to = "gdp_growth") %>%
  rename(Country=country) %>%
  mutate(Year =as.numeric(Year)) %>%
  #  tidyr::drop_na() %>% # not sure we need this..
  rename(`GDP Growth/Capita (%)` = gdp_growth)
```

Now let's see how this data has changed:

```{r}
gdp_growth %>%
  head()

gdp_growth %>%
  count(Year)
```

### Energy Use per Person

```{r}
energy_use %>%
  head()
```

```{r}
energy_use %>%
  glimpse()
```


```{r}
energy_use %<>%
  pivot_longer(cols = -country, names_to = "Year", values_to = "energy_use") %>%
  rename(Country=country) %>%
  mutate(Year = as.numeric(Year)) %>%
  rename(`Energy Use (kg, oil-eq./capita)` = energy_use)

```

#### {.scrollable}
```{r}
# Scroll through the output!
energy_use %>%
glimpse()
```
####

### Crude Mortality Rate

```{r}
mortality %>%
  head()
```


We can see that there are a couple of empty rows which indicate when the data was updated.
We can also see that the columns really start at the 3rd row. So first we will repace the column names with the 3rd row. Then we will remove the first 3 rows.

```{r}
colnames(mortality)
colnames(mortality) <- mortality[3,]
colnames(mortality)
mortality <- mortality[-c(1:3),]
```



#### {.scrollable}
```{r}
mortality %>%
  glimpse()

```
####


That is looking better! However, we also want to remove some variables like: `Country Code`, `Indicator Name`, and `Indicator Code`. We can do that using the `select()` functio of the `dplyr` package. We can use the minus sign `-` to indicate what variables we dont want to keep.

```{r}
mortality %<>%
  select(-`Country Code`,
         -`Indicator Name`,
         -`Indicator Code`) %>%
  rename(Country = "Country Name") %>%
  #gather(key = Year, value = `Deaths/1000 People`, -Country) %>%
  pivot_longer(cols = -Country, 
               names_to = "Year", 
               values_to = "Deaths/1000 People") %>%
  mutate(Year = as.numeric(Year)) %>%
  mutate(`Deaths/1000 People` = as.numeric(`Deaths/1000 People`))

```

```{r}
mortality %>%
  head()
```


## US-specific Data

### Disasters

```{r}
us_disaster  %>%
  head()
```

We are specifically interested in the `Year` and  the variables that contain the word `"Count"` so we will select them using the `select()` and `contains()` functions in the `dplyr` package. Since we are selecting for variables with the word `"Count"` we need to use quotation marks around it. Selecting for the variable `year` does not require this as that is actually the name of one of the existing variables.


```{r}
us_disaster %<>%
           select(Year, contains("Count"))

us_disaster %>%
  head()
```

Now we want to create a new variable that will be the sum of all the different types of disasters for each year. 

We can create this ne variable using the `mutate()` function of `dplyr` and we will use the base `rowSums()` function to perform the calculation. We dont want to include the `Year` variable in our sum, so we can exclude it using the `select`function within the `rowSums()` function. However, to do so we need to indicate that we are using the data that we already used as input to our `mutate()` and `rowSums()` functions. We can do so by using a `.`. 


```{r}
us_disaster %<>%
  mutate(`Disasters` = rowSums(select(., -Year))) 

us_disaster %>%
  glimpse()
```

```{r}
us_disaster %<>%
  dplyr::select(Year,
                `Disasters`) %>%
  mutate(Country = "United States") %>%
  pivot_longer(cols = c(-Country, - Year),
               names_to = "Indicator",
               values_to = "Value") %>%
  mutate(Region = "United States",
           Type = "US-specific")
```

### Temperature

```{r}
us_temperature <- read_csv(here("docs/temperature.txt"),skip=4,na="-99")

sapply(us_temperature, class)

us_temperature <- us_temperature %>%
  dplyr::select(-Anomaly) %>%
  mutate(Date = substr(Date, start = 1, stop = 4)) %>%
  mutate(Country="United States",
         Year = Date,
         Indicator="Temperature (Fahrenheit)",
         Region="United States",
         Type="US-specific") %>%
  dplyr::select(-Date, )
```

## Analysis data frame

```{r}
colnames(CO2_emissions)
colnames(gdp_growth)
colnames(energy_use)
colnames(mortality)

df_wide <- CO2_emissions %>%
  full_join(gdp_growth, by=c("Country", "Year")) %>%
  full_join(energy_use, by=c("Country", "Year")) %>%
  full_join(mortality, by=c("Country", "Year"))

df_long <- df_wide %>%
  gather(key=Indicator,value=Value,-Country, -Year) %>%
  mutate(Region=case_when(Country=="United States" ~ "United States",
                          Country!="United States" ~ "Rest of the World"),
         Type="Global")

setequal(sapply(df_long, class),sapply(us_disaster, class))
setequal(sapply(df_long, class),sapply(us_temperature, class))

df_long <- df_long %>%
  rbind(us_disaster) %>%
  rbind(us_temperature)

sapply(df_long,class)

df_long$Country <- as.factor(df_long$Country)
df_long$Year <- as.numeric(df_long$Year)

sapply(df_long,class)

df_long <- df_long %>%
  filter(complete.cases(.)) %>%
  arrange(Country)
```




## **Data Visualization**
*** 


### CO2 plots

We will rename the `Emissions` variable so that it will include more information in our plot.

```{r}

CO2_emissions %>%
  # rename(`CO2 Emissions (Mg)`= Emissions) %>%
ggplot(aes(x=Year, y=`CO2 Emissions (Mg)`, group=Country)) +
  geom_line(alpha=0.2) + 
  labs(title = expression("Country CO"[2]*" Emissions per Year , 1751-2014"),
       caption = expression("Limited to reporting countries")) + 
  ylab("Emissions (1M Metric Tonnes)")

CO2_world<-CO2_emissions %>%
 # rename(`CO2 Emissions (Mg)`= Emissions) %>%
  group_by(Year) %>%
  summarise(`CO2 Emissions (Mg)` = sum(`CO2 Emissions (Mg)`, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=`CO2 Emissions (Mg)`)) +
  geom_line() + 
  labs(title = expression("World CO"[2]*" Emissions per Year , 1751-2014"),
       caption = expression("Limited to reporting countries")) + 
  ylab("Emissions (1M Metric Metric Tonnes)")
CO2_world
```


### Yearly Growth in GDP per Capita

```{r}
ggplot(gdp_growth, aes(x=Year, y=`GDP Growth/Capita (%)`, group=Country)) +
  geom_line(alpha=0.2) + 
  labs(title = expression("Country GDP Growth per Capita per Year (Annual %), 1801-2019"),
       caption = expression("Limited to reporting countries")) + 
  ylab("GDP Growth per Capita (Annual %)")

gdp_growth %>%
  group_by(Year) %>%
  summarise(`GDP Growth/Capita (%)` = mean(`GDP Growth/Capita (%)`, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=`GDP Growth/Capita (%)`)) +
  geom_line() + 
  labs(title = expression("Mean Country GDP Growth per Capita per Year (Annual %), 1801-2019"),
       caption = expression("Limited to reporting countries")) + 
  ylab("GDP Growth per Capita (Annual %)")
```

### Energy Use per Person

```{r}
ggplot(energy_use, aes(x=Year, y=`Energy Use (kg, oil-eq./capita)`, group=Country)) +
  geom_line(alpha=0.2) + 
  labs(title = expression("Country Energy Use (kg of Oil Equivalent per Capita), 1960-2015",),
       caption = expression("Limited to reporting countries")) + 
  ylab("Energy Use (kg of Oil Equivalent per Capita)")

energy_use %>%
  group_by(Year) %>%
  summarise(`Energy Use (kg, oil-eq./capita)` = sum(`Energy Use (kg, oil-eq./capita)`, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=`Energy Use (kg, oil-eq./capita)`)) +
  geom_line() + 
  labs(title = expression("Worldwide Energy Use (kg of Oil Equivalent per Capita), 1960-2015"),
       caption = expression("Limited to reporting countries")) + 
  ylab("Energy Use (kg of Oil Equivalent per Capita)")
```

### Crude Mortality Rate


```{r}

ggplot(mortality, aes(x=Year, y=`Deaths/1000 People`, group=Country)) +
  geom_line(alpha=0.2) + 
  labs(title = expression("Country Crude Mortality Rate (per 1000 Persons), 1960-2019",),
       caption = expression("Limited to reporting countries")) + 
  ylab("Crude Mortality Rate (per 1000 Persons)")

mortality %>%
  group_by(Year) %>%
  summarise(`Deaths/1000 People` = mean(`Deaths/1000 People`, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=`Deaths/1000 People`)) +
  geom_line() + 
  labs(title = expression("Mean Country Crude Mortality Rate (per 1000 Persons), 1960-2019",),
       caption = expression("Limited to reporting countries")) + 
  ylab("Crude Mortality Rate (per 1000 Persons)")
```

## US-specific Data

### Disasters






```{r, eval=FALSE}
ggplot(df_long, aes(x=Year, y=Value, group=Country)) +
  geom_line(alpha=0.2) + 
  facet_grid(Indicator~., scales = "free_y") +
  ylab("Indicator Value") + 
  labs(title="Distribution of Indicators by Year and Value")
```

## Subsetting the data

```{r}
df_long %>%
  filter(Type=="Global") %>%
  group_by(Year,Indicator) %>%
  tally() %>%
  ggplot(aes(x=Year, y=n, color=Indicator)) +
  geom_vline(xintercept = 1980, linetype=2, color="black") +
  geom_vline(xintercept = 2010, linetype=2, color="black") +
  geom_line() +
  labs(title = "Countries with Complete Data per Year",
       subtitle = "Global Data") + 
  ylab("Countries") + 
  scale_x_continuous(breaks = seq(1750,2020,by=10),
                     labels = seq(1750,2020,by=10)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x = element_blank(),
        legend.position = "bottom")

df_long %>%
  filter(Region=="United States") %>%
  group_by(Indicator) %>%
  summarise(Start=min(Year), End=max(Year)) %>%
  ggplot(aes(x=Indicator, y=End)) +
  geom_hline(yintercept = 1980, linetype=2, color="black") +
  geom_hline(yintercept = 2010, linetype=2, color="black") +
  geom_segment(aes(x=Indicator,
                   xend=Indicator,
                   yend=End,
                   y=Start)) +
  geom_point(aes(x=Indicator, y=Start), shape=16, color="black") +
  geom_point(aes(x=Indicator, y=End), shape=21, fill="white", color="black") + 
  coord_flip() +
  labs(title = "Complete Data per Year",
       subtitle = "US-specific Data") + 
  ylab("Countries") + 
  scale_y_continuous(breaks = seq(1750,2020,by=10),
                     labels = seq(1750,2020,by=10)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank())
```

```{r, Animation_1, warning=FALSE, eval=FALSE}
animation_1 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Deaths/1000 People") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region,alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() +
  ylab("Crude Mortality Rate") +
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_1, fps = 10, duration = 5)
```

```{r, Animation_2, warning=FALSE, eval=FALSE}
animation_2 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Energy Use (kg, oil-eq./capita)") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90)) + 
  ylab("Energy Use per Capita") +
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_2, fps = 10, duration = 5)
```

```{r, Animation_3, warning=FALSE, eval=FALSE}
animation_3 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="GDP Growth/Capita (%)") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90)) +
  ylab("GDP Growth per Capita (%)") +
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_3, fps = 10, duration = 5)
```

```{r, Animation_4, warning=FALSE, eval=FALSE}
animation_4 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="CO2 Emissions (Mg)") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title="Distribution of Indicators by Year and Value, 1980-2010") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90)) +
  ylab("CO2 Emissions (Mg)") +
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_4, fps = 10, duration = 5)
```

```{r}
Top10<-df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="CO2 Emissions (Mg)") %>%
  filter(Year>=1900) %>%
  filter(Year<=2010) %>%
  group_by(Country) %>%
  mutate(max_val = max(Value)) %>%
  ungroup() %>%
  mutate(rank=dense_rank(-max_val)) %>%
  filter(rank<=10) %>%
  ggplot(aes(x=Year, y=fct_reorder(Country, Value, max))) +
  geom_tile(color="transparent", aes(fill=log(Value))) +
  scale_fill_gradientn(colors = c("yellow","red","black")) +
  scale_x_continuous(breaks = seq(1900,2010,by=5),
                     labels = seq(1900,2010,by=5)) + 
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank(),
        legend.position = "bottom") + 
  labs(title = "Top 10 Emissions-producing Countries in 2010 (1900-2010)",
       subtitle = "Ordered by Emissions Produced in 2010",
       fill = "Ln(CO2 Emissions (Mg))")

Top10
```

## US-specific

```{r}
df_long_us <- df_long %>%
  filter(Country=="United States")

# Approximated derivative function)
df_long_us <- df_long_us %>%
  filter(Year>=1900,
         Year<=2010) %>%
  group_by(Indicator) %>%
  mutate("Change (%)"=((Value/lag(Value))*100)-100,
         Mean=mean(Value),
         Anomaly=Value-Mean) %>%
  ungroup() %>%
  mutate(Anomaly_color=ifelse(Anomaly>0,"Positive",
                              ifelse(Anomaly<0,"Negative","Zero")),
         Anomaly_color=factor(Anomaly_color, levels = c("Positive",
                                                        "Negative",
                                                        "Zero"),
                              ordered = TRUE))
```

```{r}
US_Indicators<-df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value)) + 
  geom_line() + 
  facet_wrap(Indicator~., ncol=2, nrow=3, scales = "free_y") + 
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) +
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title.y = element_blank()) + 
  labs(title = "US-specific Indicators (1980-2010)")
US_Indicators
```

```{r}
df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=`Change (%)`, color=Indicator, fill="transparent")) + 
  geom_hline(yintercept=0.8, linetype=2) +
  geom_hline(yintercept=1.2, linetype=2) +
  geom_hline(yintercept = 1, linetype=3) +
  geom_line(size=0.5) + 
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) +
  scale_y_continuous(breaks = seq(-500,1250, by=250),
                     labels = seq(-500,1250, by=250)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "US-specific Indicators (1980-2010)",
       subtitle = "Change (%) Lines")

df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  filter(Indicator=="Temperature (Fahrenheit)"|
           Indicator=="Energy Use (kg, oil-eq./capita)"|
           Indicator=="CO2 Emissions (Mg)") %>%
  ggplot(aes(x=Year, y=`Change (%)`, color=Indicator)) + 
  geom_hline(yintercept=0.8, linetype=2) +
  geom_hline(yintercept=1.2, linetype=2) +
  geom_hline(yintercept = 1, linetype=3) +
  geom_line(size=1) +
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) +
  scale_y_continuous(breaks = seq(-10,10, by=1),
                     labels = seq(-10,10, by=1),
                     limits = c(-10,10)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "Emissions, Energy Use, and Temperature (1980-2010)",
       subtitle = "Change (%) Lines")

df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  filter(Indicator=="Temperature (Fahrenheit)"|
           Indicator=="Energy Use (kg, oil-eq./capita)"|
           Indicator=="CO2 Emissions (Mg)") %>%
  ggplot(aes(x=Year, y=`Change (%)`, color=Indicator)) + 
  geom_hline(yintercept=0.8, linetype=2) +
  geom_hline(yintercept=1.2, linetype=2) +
  geom_hline(yintercept = 1, linetype=3) +
  geom_smooth(size=1, alpha=0.1, aes(fill=Indicator), se=FALSE) +
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) +
  scale_y_continuous(breaks = seq(-10,10, by=1),
                     labels = seq(-10,10, by=1),
                     limits = c(-10,10)) +
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "US Emissions, Energy Use, and Temperatures (1980-2010)",
       subtitle = "Smoothed Change (%) Lines")
```

```{r}
df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  filter(Indicator=="CO2 Emissions (Mg)"|
           Indicator=="Temperature (Fahrenheit)") %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() + 
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) + 
  facet_wrap(Indicator~., scales = "free_y", ncol=1) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank()) + 
  labs(title="US Emissions and Temperatures (1980-2010)")

df_long_us %>%
  filter(Indicator=="CO2 Emissions (Mg)"|
           Indicator=="Temperature (Fahrenheit)") %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_vline(xintercept = 1980, linetype=2, color="black") + 
  geom_vline(xintercept = 2010, linetype=2, color="black") +
  geom_segment(aes(x=Year, y=Value, xend=Year, yend=Mean,color=Anomaly_color), size=1.25) +
  scale_color_manual(values = c("red","blue","gray")) + 
  geom_hline(aes(yintercept=Mean), linetype=1, color="black") +
  scale_x_continuous(breaks = seq(1900,2010,by=5),
                     labels = seq(1900,2010,by=5)) +
  facet_wrap(Indicator~., scales = "free_y", ncol=1) + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank(),
        legend.position = "none")  +
  labs(title = "US Emissions and Temperatures (1900-2010)",
       subtitle = "Indicator Mean Represented by Solid Black Line")
```

```{r}
df_long_us %>%
  filter(Indicator=="Temperature (Fahrenheit)"|
           Indicator=="CO2 Emissions (Mg)") %>%
  ggplot(aes(x=Year, y=`Change (%)`)) +
  annotate("rect", xmin=-Inf, xmax=Inf, ymin=0, ymax=Inf, alpha=0.25, fill="green") +
  annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=0, alpha=0.25, fill="red") +
  geom_hline(yintercept=0, linetype=1) + 
  geom_segment(aes(x=Year, y=`Change (%)`, xend=Year, yend=0), size=1.25) +
  facet_wrap(Indicator~., scales = "free_y", ncol=1) +
  scale_x_continuous(breaks = seq(1900,2010,by=5),
                     labels = seq(1900,2010,by=5)) +
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "US Emissions, Energy Use, and Temperatures (1980-2010)",
       subtitle = "Change (%) Lines")
```

## Main plot

```{r}

library(patchwork)

CO2_world + Top10 + US_Indicators +
  plot_layout(widths = c(1, 2), heights = unit(c(2, 5), c('cm', 'null')))

png(here::here("img", "mainplot.png"), width = 900, height = 700)
(CO2_world | Top10)/ US_Indicators+
    plot_layout(widths = c(1, 2), heights = unit(c(4, 5), c('cm', 'null')))
dev.off()
```


